Identification of Obstacles to Culture–Tourism Integration and Revitalization Strategies for Traditional Villages from the Perspective of Cultural Landscape Genes: A Case Study of Dayuwan Village

Abstract

Traditional villages embody regional culture and local knowledge, yet culture–tourism integration often suffers from a mismatch between resource value and effective transformation. To address this problem, this study proposes a two-dimensional “benefit–obstacle” diagnostic and strategy-matching framework and tests its case-based applicability in Dayuwan Village. First, a cultural landscape gene (CLG) atlas was constructed for the village based on a geo-information coding scheme, covering both tangible and intangible CLGs. Second, a four-dimensional evaluation system was operationalized through five expert judgments and 106 valid on-site questionnaires collected from tourists (n = 67) and residents (n = 39). Criterion weights were determined using an AHP–entropy combination approach, and the comprehensive benefit closeness coefficient was calculated via TOPSIS. Third, an obstacle degree identification model was employed to pinpoint key constraints and derive composite obstacle degrees. Results within the Dayuwan case show that the TOPSIS closeness coefficients of the 17 genes ranged from 0.653 to 0.782 (mean = 0.714), with 4, 6, and 7 genes classified as excellent, good, and medium, respectively; composite obstacle degrees ranged from 0.0228 to 0.1975. In Dayuwan Village, higher obstacle degrees clustered mainly in intangible CLGs, whereas Ming–Qing architecture and frequently practiced folk-cultural genes showed comparatively lower obstacle degrees. The transformation process is constrained by four mechanisms—landscape character protection, economic transformation, social identity, and market demand—with economic transformation constraints being the most prominent. Based on the benefit–obstacle matrix, 17 CLGs were classified into five activation scenarios and matched with corresponding revitalization strategies. This framework links benefit ranking, obstacle diagnosis, and strategy matching, and provides a case-based diagnostic reference for the conservation and culture–tourism integration of villages with comparable heritage conditions, subject to local recalibration of indicators, weights, and thresholds.

1. Introduction

Traditional villages carry regional culture and local knowledge and serve as important carriers for understanding Chinese architecture, society, and culture [1,2]. Under the national strategy that simultaneously promotes rural revitalization and culture–tourism integration, the conservation and development of traditional villages have brought a certain degree of spatial restoration and visitor agglomeration, yet insufficient heritage excavation, interpretation, and marketing have also generated deeper problems such as homogeneous development [3]. In Hubei Province, for example, the marked heterogeneity of traditional villages has not always been translated into differentiated development practices and is still often associated with extensive resource use, weak storytelling, and insufficient benefit conversion. Recent policies have continuously emphasized the need to build on cultural distinctiveness and to coordinate conservation with revitalization, thereby providing clearer guidance for refined cultural resource development and tiered utilization. In this context, cultural landscape gene theory provides important theoretical support for preserving local cultural memory and sense of place.

Since the early twenty-first century, when scholars such as Liu Peilin first proposed cultural landscape gene theory, the concept has continued to evolve [4,5]. In recent years, with the support of interdisciplinary research and digital technologies, it has become an active topic in China [6,7]. Discussions have gradually moved from settlement imagery, geomantic patterns, and information-unit representation toward a theoretical and methodological system for identification, coding, and atlas construction, and the concept has been extended to heritage conservation, spatial form optimization, and culture–tourism integration development [8]. Existing methods have been applied to landscape extraction and gene atlas construction in traditional villages and have generated relatively mature cases in different regional village types and regional contexts [9,10,11]. Further studies have also explored spatial connectivity, development potential, and historic layering in clustered village areas [12,13]. With growing attention to culture–tourism integration and development performance, related studies have gradually turned to the evaluation of integration pathways, spatial quality, and rural revitalization effects [14,15,16]. Heritage tourism sustainability has likewise become an important analytical focus in related cultural-resource evaluation [17]. However, existing studies on traditional villages and cultural landscape genes have focused primarily on gene identification and atlas construction, spatial connectivity and historical layering, and the evaluation of integration pathways and spatial quality. Relatively fewer studies have examined how identified heritage attributes are translated into tourism products, interpreted experientially, and matched with market demand in specific village contexts.

Related international studies usually identify and evaluate comparable objects under concepts such as “heritage elements” or “landscape characteristics”, thereby forming analytical frameworks such as Historic Urban Landscape, Landscape Character Assessment, and authenticity-centered heritage governance [18,19,20]. Historic Landscape Characterisation has further contributed technical approaches for reading historical layering and spatial character [21]. At the same time, heritage tourism studies have examined how historic urban environments and heritage destinations are interpreted, experienced, and managed in tourism development [22,23]. In heritage interpretation and cultural tourism research, interpretation, representation, and participation are widely regarded as key links between cultural resources and visitor experience [24,25]. Building on these lines of inquiry, Chinese scholars have proposed and developed the concept of “cultural landscape genes”, defining them as structured, identifiable, and codable information units of cultural landscape heritage elements in traditional villages. Accordingly, this study adopts the established concept of “cultural landscape genes” (hereafter, “genes”). The term refers to codable and identifiable cultural units within the broader international discussions of heritage attributes, landscape character, and heritage interpretation.

Methodologically, the indicator-weighting methods commonly used in culture–tourism integration studies include the analytic hierarchy process (AHP), TOPSIS, and the entropy method [26,27,28]. AHP can incorporate expert judgment and provides a clear hierarchical structure; TOPSIS produces intuitive relative rankings under conditions of multiple indicators with different units; and the entropy method objectively reflects indicator variation. Although these methods are widely used, ranking-oriented approaches alone are less able to reveal how shortcomings are formed or to support precise intervention.

In terms of evaluation dimensions, recent studies have shown that authenticity is a key factor influencing heritage perception, experience quality, and revisit intention [29,30,31]. Place attachment and place identity further shape how people understand, internalize, and emotionally connect with heritage places [32,33]. Meanwhile, residents’ perception, participation, and support are increasingly recognized as indispensable components of sustainable heritage and tourism governance [34,35,36]. Related studies in Chinese heritage settings have also shown that local use and perception remain important for the long-term coordination of conservation and tourism [37].

International frameworks and empirical studies also emphasize process governance, stakeholder collaboration, and the differentiated effects of heritage designation and tourism development on local communities [38,39,40]. In addition, the experiential core of heritage tourism and visitors’ routes of meaning-making have been discussed from the perspectives of heritage motivation, mindfulness, and tourism experience [41,42,43]. Perceived historical authenticity has further deepened understanding of how visitors judge and consume heritage [44]. Recent studies have also highlighted the role of heritage interpretation and guided explanation in enhancing perceived value and sustainable development [45,46]. Heritage storytelling and intangible cultural heritage tourism have broadened the discussion from static display to narrative communication and living-cultural activation [47,48]. Perceived authenticity in intangible cultural heritage tourism has likewise become an increasingly important analytical dimension [49]. At the same time, computational methods, digital documentation, and augmented reality have expanded the technical toolkit for the conservation, presentation, and activation of rural cultural heritage [50,51,52].

The obstacle degree model provides an effective complement to ranking-oriented evaluation. Recent studies have demonstrated its strong bottleneck-identification capacity and decision-support value in rural revitalization and related evaluation systems [53]. Against this background, this study addresses two research questions. First, how can an evaluation system for cultural landscape genes oriented toward culture–tourism transformation be constructed so that the indicators simultaneously reflect four dimensions of value, namely cultural scarcity, activatability, market demand, and cultural identity? Second, how can comprehensive benefit and obstacle degree be effectively coupled and then used for scenario classification and strategy matching so that evaluation results can be translated into differentiated governance pathways? Based on the foregoing literature, three case-based analytical expectations are proposed. A1: Cultural landscape genes with higher activatability and market demand are expected to show higher comprehensive benefit closeness than genes whose value is mainly historical-symbolic. A2: Intangible genes are expected to be more likely than tangible genes to exhibit composite obstacles because their transformation depends simultaneously on interpretation, identity communication, and demand matching. A3: Within the Dayuwan case, activatability is expected to be the most frequently observed primary obstacle among tangible genes.

Accordingly, this study followed four analytical steps: cultural landscape gene atlas construction, comprehensive benefit evaluation, obstacle diagnosis, and revitalization strategy matching. The analysis covered both tangible and intangible genes and simultaneously considered historical value, social perception and identity, and realistic transformation potential, thereby ensuring multidimensionality and contextual adaptability. In terms of weighting, the study integrated AHP, the entropy method, and TOPSIS to rank the comprehensive benefits of cultural landscape genes and then introduced an obstacle degree identification model to diagnose transformation barriers at the criterion level, such as weak economic adaptability, low cultural identity, and insufficient functional matching of culture–tourism transformation carriers. Based on this, a two-dimensional benefit–obstacle diagnostic and strategy-matching framework was constructed, providing a case-based analytical tool for the integrated development of culture and tourism in traditional villages. Dayuwan Village was used as the empirical case, and the full methodological application is shown in Figure 1.

This study makes three main contributions. First, it moves cultural landscape gene research from descriptive identification toward case-based transformation diagnosis. Second, it integrates weighting, ranking, and obstacle attribution into a single analytical sequence that connects evaluation with intervention logic. Third, it translates diagnostic heterogeneity into scenario-specific governance and revitalization pathways rather than one-size-fits-all recommendations.

2. Materials and Methods

2.1. Study Area

Dayuwan Village is located in the Mulan Ecological and Cultural Tourism Core Area of Huangpi District and belongs to Wuhan’s “one-hour tourism economic circle”. It is a typical lineage-based traditional village in the middle reaches of the Yangtze River [54,55,56] (Figure 2).

The village lies west of the She River ecological corridor and north of Mulan Mountain, a well-known Taoist site. Seventy-five Ming–Qing historic buildings and related relics remain in the village, and the exterior walls of old residences preserve numerous hand-painted polychrome murals from the Qing Dynasty, demonstrating the village’s long history and profound cultural accumulation (Figure 3).

2.2. Construction of the Cultural Landscape Gene Atlas

Based on cultural landscape gene theory, this study structured and managed the genes of Dayuwan Village according to a geo-information coding scheme of “category-major class-minor class-cultural symbol”. To ensure the operability and comparability of subsequent evaluation, gene identification and coding adopted a “minimum sufficient set” approach with two parallel branches: tangible genes and intangible genes. Drawing on Huangpi District Gazetteer, Master Plan for Dayuwan Historic and Cultural Village in Huangpi District, Wuhan, and related yearbooks, and cross-validating these materials through field investigation, the study formally interpreted the composition and identification criteria of cultural landscape genes at the levels of elements, rules, and parameters, thereby systematically analyzing the cultural landscape gene genealogy of Dayuwan Village.

The identification and coding process consisted of source verification, independent double coding, and expert review. Local gazetteers, the master plan, and field survey materials served as the principal basis, supplemented by photographic and surveying data (Figure 4). Two researchers coded the material independently using the same coding framework. Before consensus discussion, inter-coder reliability was assessed using Cohen’s kappa based on the initial coding results. For the 38 coded units, the agreement reached 92.1% (Cohen’s kappa = 0.861) at the major-category level and 84.2% (Cohen’s kappa = 0.781) at the gene-code level, indicating strong and substantial agreement, respectively (Appendix A

Table A1. Inter-coder reliability of the CLG coding procedure.

Coding Level	No. of Units	Percent Agreement	Cohen’s Kappa	Interpretation
Major-category level	38	92.10%	0.861	Strong agreement
Gene-code level	38	84.20%	0.781	Substantial agreement

). Initial coding disagreements were then checked item by item and resolved through consensus review. The revised coding results were subsequently verified by local culture and planning experts, thereby enhancing methodological transparency, consistency, and reproducibility.

On this basis, eight major categories and 17 genes were identified. The tangible category includes architectural form, spatial pattern, decorative art, and production facilities, while the intangible category includes clan culture, farming-and-reading culture, folk culture, and oral history (

Table 1. Classification List of Cultural Landscape Genes in Dayuwan Village.

Gene Category	Gene Identification	Coded Item	Description
A1 Tangible cultural landscape genes	A11 Architectural form genes	A111 Ming–Qing residential building complex	A1111 Seventy-five patio-courtyard-style buildings dating from the Ming–Qing period to the Republic of China period
			A1112 Three-sided courtyard layout, hard-gable double-pitched roof, and “drip-line stone wall” construction technique
			A1113 Brick-and-timber projecting door lintels
		A112 Huizhou-style architectural features	A1121 White walls, black tiles, and horse-head gable forms
			A1122 Dry-laid bluestone walls
			A1123 Jingchu dwelling structure with a “front wall wrapping the rear eaves”
	A12 Spatial pattern genes	A121 Feng shui residential pattern	A1211 Ecological landscape pattern of “storing wind and gathering qi”
			A1212 Settlement siting of “pond in front and mountain behind”
		A122 Street-and-lane spatial pattern	A1221 Comb-shaped street system: more than 20 bluestone lanes and a “seven horizontal, nine vertical” road network
			A1222 Serial layout of “courtyard within courtyard, lane connected to lane”
		A123 Functional zoning	A1231 Dayuwan Museum as the center, surrounded by the historic building cluster
			A1232 Shuikou Garden, opera stage, and smoking hall
			A1233 Remains of part of the Ming-era earthen wall retained on the village periphery
	A13 Decorative genes	A131 Mural art	A1311 Qing Dynasty hand-painted murals preserved on exterior walls
		A132 Wood carving and stone carving art	A1321 Decorative motifs such as the swastika pattern and ice-crack pattern
			A1322 Column bases and thresholds; “rhinoceros gazing at the moon” column base
	A14 Production facility genes	A141 Traditional handicrafts	A1411 Pottery kiln site
			A1412 Farming facilities: stone mills, ancient wells, and other agricultural tools
B1 Intangible cultural landscape genes	B11 Clan culture genes	B111 Clan system	B1111 Yu genealogy recording more than 100 officials who entered office through the imperial examination
			B1112 “Three Elders Council” lineage deliberation system
			B1113 Ancestral tomb complex and the lineage memory of “three prefects from one family”
		B112 Family precepts and family ethos	B1121 Family ethic embodied in the gate layout of “wide outside, narrow inside” and the principle of “wide entry, narrow exit; diligence and frugality”
			B1122 Family instruction of “farming and reading as the foundation of the household”
	B12 Farming–and–reading culture genes	B121 Imperial examination tradition	B1211 Prosperity of “four ministers in five generations”
			B1212 Preserved remains of a Qing Dynasty private school
		B122 Farming techniques	B1221 Jingchu farming customs
			B1222 Ecological agricultural model
	B13 Folk culture genes	B131 Intangible heritage skills	B1311 Huangpi “Jiang Shizi” lion performance and Huangpi opera
			B1312 Traditional handicrafts
			B1313 Huangpi shadow puppetry
			B1314 Stone-carved architectural component craftsmanship
		B132 Festivals and rituals	B1321 Temple fairs and Qingming ancestor worship
			B1322 Dayuwan’s four-season farming-and-reading themed “Autumn Drying” activity
		B133 Food culture	B1331 Handmade dousi baking and the custom of making ciba
	B14 Oral history genes	B141 Migration memory	B1411 Migration legends
			B1412 Feng shui legend related to village siting
		B142 Folk beliefs	B1421 Ritual of reporting to the ancestors
			B1422 Kiln-god worship and related beliefs

, Figure 5).

2.3. Evaluation Indicator System and Questionnaire Design

After constructing the cultural landscape gene atlas, five experts were selected for the AHP judgment process according to three criteria: relevant research or professional experience in traditional village conservation, familiarity with local heritage resources, and direct engagement with planning, management, or interpretation practice in the study area. These experts were invited to provide pairwise judgments on the relative importance of the four criterion-level indicators in the AHP framework: I1, cultural scarcity; I2, activatability; I3, market demand; and I4, cultural identity (

Table 2. (a) Measurement scale and item statements for the four diagnostic dimensions. (b) Four-Dimensional Indicator Mean Matrix.

(a)
Indicator Code	Meaning	Item Statement
I1	Cultural scarcity	This gene is scarce and irreplaceable within this village or among similar traditional villages.
I2	Activatability	This gene can be operationally translated into culture–tourism products or experiences.
I3	Market demand	This gene can attract tourists and stimulate tourism consumption, thereby increasing their willingness to pay, stay longer, and revisit.
I4	Cultural identity	This gene can trigger understanding, recognition, and emotional resonance with the village culture.
(b)
CLG Code	CLG Name	I1	I2	I3	I4
A111	Ming–Qing residential building cluster	4.73	4.18	4.45	4.58
A112	Integrated Huizhou-style architectural features	4.61	4.05	4.32	4.51
A121	Fengshui-based living pattern	4.15	3.63	3.74	4.75
A122	Street–alley spatial pattern	3.93	3.32	3.44	4.64
A123	Functional zoning	3.72	3.43	3.64	4.53
A131	Mural art	4.31	3.53	3.84	4.52
A132	Wood and stone carving art	4.43	3.72	3.94	4.65
A141	Traditional handicrafts	4.06	4.36	4.23	4.43
B111	Patriarchal/clan institutions	4.03	3.22	3.54	4.83
B112	Family precepts and family ethos	4.24	3.84	4.05	4.82
B121	Imperial examination tradition	4.25	2.92	3.13	4.74
B122	Farming skills	3.83	4.44	4.33	4.24
B131	Intangible heritage skills	4.52	4.48	4.63	4.35
B132	Festivals and rituals	4.33	4.55	4.72	4.26
B133	Food culture	3.94	4.61	4.52	4.34
B141	Migration memory	3.62	3.03	3.34	4.44
B142	Folk beliefs	3.53	2.82	3.24	4.32

). The criterion-level setting was also informed by the official evaluation and identification indicator system for traditional villages, which emphasizes the coordinated consideration of heritage value, distinctiveness, and protection-oriented utilization [57]. To obtain the performance values of each cultural landscape gene on I1–I4, the study designed and administered a ‘Four-Dimensional Evaluation Questionnaire for Cultural Landscape Genes’ to both tourists and residents.

The indicator system was informed by previous studies on traditional village conservation, culture–tourism integration, authenticity, place identity, and heritage experience. Each gene corresponded to four items, one for each dimension, in order to control questionnaire length while maintaining matrix comparability. This instrument was designed for diagnostic scoring and cross-gene comparison at the case level; accordingly, the four dimensions were treated as analytical criteria rather than multi-item psychometric constructs. All items used a five-point scale: 1 = very low, 2 = relatively low, 3 = moderate, 4 = relatively high, and 5 = very high.

To ensure comparability across respondent groups, tourists and residents used the same items and the same scale anchors, differing only in response instructions. A total of 120 questionnaires were distributed, of which 106 were valid, yielding an effective response rate of 88.33%. The final sample comprised 67 tourists and 39 local residents. Among the tourists, 59 were from Wuhan, indicating that the visitor sample was primarily drawn from the nearby urban market. The resident sample included local retail and catering operators and other village-related practitioners, thereby reflecting the views of groups directly involved in Dayuwan’s culture–tourism integration (Figure 6). The mean score of each gene on I1–I4 was then used as the basic dataset for the entropy method, TOPSIS, and obstacle degree identification.

Instrument evaluation in this study focused on content validity, internal consistency, criterion-level structural adequacy, and cross-group stability.

First, evidence for content validity was provided through three steps: grounding the four criterion dimensions in prior studies on traditional village conservation, culture–tourism integration, authenticity, place identity, and heritage experience; aligning the criterion-level setting with the official evaluation and identification indicator system for traditional villages; and further reviewing the questionnaire structure through the five experts involved in the AHP judgment process.

Second, reliability was assessed using Cronbach’s alpha based on the 106 valid questionnaires. For diagnostic comparison purposes, the overall 68-item response matrix showed acceptable-to-good internal consistency (Cronbach’s alpha = 0.877). At the dimension-block level, Cronbach’s alpha values were 0.812 for cultural scarcity (I1), 0.739 for activatability (I2), 0.833 for market demand (I3), and 0.794 for cultural identity (I4), indicating acceptable to good internal consistency for case-based diagnostic use.

Third, to assess criterion-level structural adequacy, respondent-level mean scores on the four dimensions were subjected to the Kaiser–Meyer–Olkin (KMO) and Bartlett’s test of sphericity. The results showed KMO = 0.641 and Bartlett’s test: χ² = 46.652, df = 6, p < 0.001, suggesting that the four-dimensional criterion structure was adequate for subsequent comparative analysis in this case-based diagnostic setting.

Fourth, to examine stability across respondent groups, tourist and resident subsamples were compared using Mann–Whitney U tests on respondent-level dimension means. No significant between-group differences were detected (I1: p = 0.520; I2: p = 0.535; I3: p = 0.597; I4: p = 0.987). In addition, tourist and resident subsamples exhibited a high degree of concordance in gene-level rankings across the four dimensions (Spearman’s rho = 0.945 for I1, 0.939 for I2, 0.993 for I3, and 0.888 for I4) as shown in Appendix A

Table A2. Cross-group stability of tourist and resident responses.

Dimension	Tourists (n = 67), Mean ± SD	Residents (n = 39), Mean ± SD	Mann–Whitney U	p Value	Spearman’s Rho for Gene-Level Rankings
I1 Cultural scarcity	4.107 ± 0.416	4.172 ± 0.357	1208	0.52	0.945
I2 Activatability	3.758 ± 0.397	3.798 ± 0.356	1211.5	0.535	0.939
I3 Market demand	3.927 ± 0.442	3.980 ± 0.426	1225.5	0.597	0.993
I4 Cultural identity	4.531 ± 0.284	4.519 ± 0.311	1303.5	0.987	0.888

. These results support the internal consistency, cross-group stability, and practical adequacy of the instrument for case-based diagnostic comparison (

Table 3. Reliability and adequacy checks of the questionnaire instrument.

Check Category	Statistic	Result	Interpretation
Overall internal consistency	Cronbach’s alpha	0.877	Good
I1 Cultural scarcity	Cronbach’s alpha	0.812	Good
I2 Activatability	Cronbach’s alpha	0.739	Acceptable
I3 Market demand	Cronbach’s alpha	0.833	Good
I4 Cultural identity	Cronbach’s alpha	0.794	Acceptable to good
Criterion-level structural adequacy	KMO	0.641	Acceptable
Bartlett’s test of sphericity	χ2 = 46.652, df = 6	p < 0.001	Significant
Cross-group stability	Mann–Whitney U tests	All p > 0.05	No significant tourist–resident difference
Cross-group rank concordance	Spearman’s rho	0.888–0.993	High concordance

).

2.4. Combined Weighting Method

The value evaluation of traditional villages involves both qualitative judgment on historical-cultural value and quantitative feedback from markets and audiences. Therefore, this study adopted a combined AHP-entropy weighting method to construct an integrated evaluation scale that balances subjective and objective information and avoids the one-sidedness of a single weighting method.

First, AHP was used to build the evaluation indicator system based on the four criterion dimensions of cultural scarcity, activatability, market demand, and cultural identity. Expert knowledge was introduced to form the initial subjective weight structure, thereby ensuring that the evaluation system reflected the cognitive structure of cultural attributes. A judgment matrix was constructed to assess the relative importance of indicators that are difficult to quantify directly, such as cultural scarcity. The consistency ratio of the matrix was calculated as CR < 0.1, indicating that the consistency test was passed (

Table 4. (a) AHP judgment matrix and criterion weights (CR = 0.038 < 0.1). (b) Sub-criterion-level indicators and weights (CR = 0.029 < 0.1).

(a)
Criterion-Level	I1 Cultural Scarcity	I2 Activatability	I3 Market Demand	I4 Cultural Identity	Weight (W)
I1	1	3	2	4	0.3
I2	1/3	1	1/2	2	0.25
I3	1/2	2	1	3	0.25
I4	1/4	1/2	1/3	1	0.2
(b)
Criterion-Level		Sub-Criterion-Level			Weight (W)
I1 Cultural scarcity		C1 Uniqueness			0.545
I1 Cultural scarcity		C2 Historical value			0.309
I1 Cultural scarcity		C3 Cultural identifiability			0.146
I2 Activatability		C4 Technical feasibility			0.600
I2 Activatability		C5 Economic rationality			0.400
I3 Market demand		C6 Strength of tourist demand			0.500
I3 Market demand		C7 Market development potential			0.500
I4 Cultural identity		C8 Villagers’ identity			0.500
I4 Cultural identity		C9 Social influence			0.500

).

Second, the entropy method was introduced to calculate the entropy value and coefficient of variation of each indicator from sample data, thereby producing objective weights that reflect the dispersion and information contribution of indicators and correcting possible subjectivity in expert scoring. To balance the “structural knowledge of expert judgment” (AHP) and the “objective discriminability of sample data” (entropy method), this study adopted linear combination weighting to construct the comprehensive weight:

$${\mathrm{w}}_{\mathrm{j}}=\mathsf{\alpha }{\mathrm{w}}_{\mathrm{j}}^{\mathrm{A}\mathrm{H}\mathrm{P}}+(1-\mathsf{\alpha }){\mathrm{w}}_{\mathrm{j}}^{\mathrm{E}}$$

(1)

where $W_j^{AHP}$ is the AHP subjective weight of indicator $j, W_j^E$ is its entropy-based objective weight, and $\alpha \in \left[0,1\right]$ is the preference coefficient between subjective and objective weighting. A larger $\alpha$ indicates greater emphasis on expert judgment, whereas a smaller $\alpha$ indicates greater emphasis on data dispersion.

In the evaluation of culture–tourism transformation, both expert knowledge and data-driven sample dispersion are substantively informative. Given that this study aims at case-based diagnosis rather than parameter optimization, and that neither source provides a clear a priori basis for dominance in a single-case setting, $\alpha$ = 0.5 was adopted as a symmetry-based and theoretically neutral baseline for combining subjective and objective weights. This choice was intended to ensure methodological transparency and balance, without implying that $\alpha$ = 0.5 necessarily yields optimal model performance.

To assess whether this baseline specification materially affects the results, comprehensive weights and TOPSIS-based comprehensive-benefit rankings were recalculated under three representative specifications: $\alpha =0.3$ (greater emphasis on objective information), $\alpha =0.5$ (baseline), and $\alpha =0.7$ (greater emphasis on subjective information) (

Table 5. Variations in weights under different values of $\mathsf{\alpha }$.

$\mathsf{\alpha }$	I1 Weight	I2 Weight	I3 Weight	I4 Weight
0.3	0.2398	0.2543	0.2494	0.2566
0.5	0.257	0.2531	0.2496	0.2403
0.7	0.285	0.2489	0.2501	0.216

). The results show that the comprehensive-benefit rankings remained highly stable across all pairwise comparisons among $\alpha =0.3$, 0.5 and $0.7$ (Spearman’s $\mathsf{\rho }\ge 0.99,p<0.01$). Moreover, the set of highest-ranked genes (B131, A111, B132, A112, and B133) and lowest-ranked genes (B142, B141, B121, A123, and A122) remained unchanged across all specifications.

These checks show that varying $\alpha$ does not materially change the substantive conclusions or the identification of the key diagnostic objects. Rather, it provides a theoretically balanced and empirically stable compromise between expert judgment and indicator dispersion. Therefore, $\alpha =0.5$ was retained as the baseline setting in this study.

After substituting the data for cultural scarcity, activatability, market demand, and cultural identity for the 17 genes, the comprehensive weights of the four criterion-level indicators, $W_j\left(j=1,\mathrm{2,3},4\right)$, were obtained by integrating AHP subjective weights and entropy objective weights. These weights were then used as the unified input for subsequent TOPSIS evaluation and obstacle degree identification. For descriptive comparison on the original questionnaire scale, a weighted comprehensive score for each gene was also calculated as:

$${\mathrm{S}}_{\mathrm{i}}=\sum _{\mathrm{j}=1}^4{\mathrm{W}}_{\mathrm{j}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}$$

(2)

where $x_{ij}$ denotes the mean score of gene $i$ on indicator $j$ (I1–I4). This score was used only for descriptive comparison; the benefit ranking and level classification in this study were based on the comprehensive benefit closeness coefficient derived from TOPSIS.

The results show that five genes had high weighted comprehensive scores (${\mathrm{S}}_{\mathrm{i}}$ > 4.3): A111, A112, B131, B132, and B133, accounting for 29.4% of the total. These were mainly tangible genes such as architectural form and decoration, as well as folk-cultural genes with high participation frequency, such as festivals and food culture. Five genes had low weighted comprehensive scores (${\mathrm{S}}_{\mathrm{i}}$ < 3.8): A122, A123, B121, B141, and B142, also accounting for 29.4%; three of these five were intangible genes.

2.5. TOPSIS Model

After determining the weights, TOPSIS was introduced to evaluate the comprehensive benefit level of each cultural landscape gene in relation to culture–tourism transformation. This method enables the transition from qualitative description to quantitative grading, thereby helping identify advantageous resources and supporting the prioritization of revitalization. TOPSIS evaluates the relative merit of each object by measuring its geometric distance from the positive ideal solution and the negative ideal solution.

The closeness coefficient is defined as:

$${\mathrm{C}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{D}}_{\mathrm{j}}^{-}}{{\mathrm{D}}_{\mathrm{j}}^{+}+{\mathrm{D}}_{\mathrm{j}}^{-}}}, {\mathrm{C}}_{\mathrm{j}}\in \left[0,1\right]$$

(3)

where ${\mathrm{C}}_{\mathrm{j}}$ is the closeness of evaluation object $\mathrm{j}$ to the positive ideal solution. A larger ${\mathrm{C}}_{\mathrm{j}}$ indicates a higher level of comprehensive benefit closeness for that gene.

Overall, the comprehensive benefit closeness coefficients in the raw data ranged from 0.653 to 0.782 and were concentrated in the medium-to-high interval (mean = 0.714; standard deviation = 0.041), indicating that the overall benefit level of the cultural landscape genes in Dayuwan Village was relatively high and that dispersion was limited. To improve interpretability and facilitate subsequent management application, the study combined the sample distribution with thresholds rounded to two decimal places and classified ${\mathrm{C}}_{\mathrm{j}}$ into four levels: excellent $\left({\mathrm{C}}_{\mathrm{j}}\ge 0.75\right);$ good ($0.70\le {\mathrm{C}}_{\mathrm{j}}<0.75);$ medium $\left(0.65\le {\mathrm{C}}_{\mathrm{j}}<0.70\right);$ and low benefit $\left({\mathrm{C}}_{\mathrm{j}}<0.65\right)$ (

Table 6. Classification of cultural landscape gene benefit levels.

Benefit Level	Closeness Range	Number of Genes	Representative Genes	Characteristics
Excellent benefit	${\mathrm{C}}_{\mathrm{j}}$ ≥ 0.75	4	A111, B131, B132, A112	High scarcity, strong demand, and strong transformation potential; core development carriers
Good benefit	$0.70 \le {\mathrm{C}}_{\mathrm{j}}$ < 0.75	6	A131, A132, B112, B133, A141, B122	Moderate scarcity, with development value; transformation needs to be strengthened
Medium benefit	$0.65 \le {\mathrm{C}}_{\mathrm{j}}$ < 0.70	7	A121, A122, A123, B111, B121, B141, B142	Medium demand or transformation capability; revitalization strategies need optimization
Low benefit	${\mathrm{C}}_{\mathrm{j}}$ < 0.65	0	—	Low scarcity or low demand

). No genes were classified into the low-benefit category in the Dayuwan case sample. This result suggests that variation among genes is manifested primarily in their obstacle structures and relative transformation constraints.

2.6. Obstacle Degree Identification Model

TOPSIS ranking alone cannot explain why a gene is difficult to revitalize. Therefore, this study introduced an obstacle degree model to move from comprehensive evaluation to obstacle identification and mechanism attribution. The model decomposes the shortfalls that hinder benefit conversion and quantifies the obstacle degree of each single indicator to overall benefit realization, thereby identifying key constraint factors through both the “weight contribution of indicators” and the “deviation degree of indicators”. The calculation formula is:

$$O_{ij}={\displaystyle \frac{\left(1-r_{ij}\right)w_i}{{\sum }_{i=1}^m\left(1-r_{ij}\right)w_i}}$$

(4)

where $O_{ij}$ is the obstacle degree of indicator $i$ for gene $j$, $1-r_{ij}$ is the deviation degree of the indicator, and $w_i$ is the comprehensive weight of the evaluation indicator. A higher obstacle degree indicates a stronger constraining effect of that indicator on the realization of culture–tourism transformation benefits.

The comprehensive weights ($W_j$), TOPSIS comprehensive benefit closeness coefficients ($C_j$), and obstacle degrees of the 17 genes were then calculated and analyzed (

Table 7. Integrated diagnosis results based on closeness and obstacle degree of CLGs in Dayuwan Village.

Code	Weighted Score	${\mathbf{C}}_{\mathbf{j}}$	Benefit Level	Dominant Criterion-Level Obstacle Factor (s)	Obstacle Degree	Attributed Constraint Mechanism
A111	4.4925	0.782	Excellent	Activatability (I2)	0.041	Economic transformation constraint
A112	4.3775	0.754	Excellent	Activatability (I2)	0.0475	Economic transformation constraint
A121	4.0375	0.698	Medium	Activatability (I2)	0.0685	Economic transformation constraint
A122	3.797	0.682	Medium	Activatability (I2)	0.084	Economic transformation constraint
A123	3.7895	0.675	Medium	Activatability (I2)	0.0785	Economic transformation constraint
A131	4.0395	0.705	Good	Activatability (I2)	0.0735	Economic transformation constraint
A132	4.174	0.712	Good	Activatability (I2)	0.064	Economic transformation constraint
A141	4.2515	0.733	Good	Cultural identity (I4)	0.0228	Social identity constraint
B111	3.865	0.689	Medium	Activatability (I2), Market demand (I3)	0.162	Composite constraint (economic transformation + market demand)
B112	4.2085	0.725	Good	Activatability (I2)	0.058	Economic transformation constraint
B121	3.7355	0.668	Medium	Activatability (I2), Market demand (I3)	0.1975	Composite constraint (economic transformation + market demand)
B122	4.1895	0.718	Good	Cultural scarcity (I1)	0.0702	Landscape conservation constraint
B131	4.5035	0.775	Excellent	Cultural identity (I4)	0.026	Social identity constraint
B132	4.4685	0.768	Excellent	Cultural identity (I4)	0.0296	Social identity constraint
B133	4.3325	0.741	Good	Cultural scarcity (I1)	0.0636	Landscape conservation constraint
B141	3.5665	0.661	Medium	Cultural scarcity (I1), Market demand (I3)	0.1658	Composite constraint (landscape conservation + market demand)
B142	3.438	0.653	Medium	Cultural scarcity (I1), Activatability (I2)	0.1972	Composite constraint (landscape conservation + economic transformation)

).

3. Results

3.1. Cultural Landscape Gene Identification in Dayuwan Village

The coding process identified eight major categories and 17 cultural landscape genes in Dayuwan Village. Tangible genes covered architectural form, spatial pattern, decorative art, and production facilities, while intangible genes covered clan culture, farming-and-reading culture, folk culture, and oral history. This structure captures both the material carriers and the living cultural practices of the village and provides the basis for subsequent multidimensional evaluation.

3.2. Weighting Results and Descriptive Comparison

The combined weighting results indicate that, under the baseline condition of $\alpha$ = 0.5, the weights of I1-I4 were 0.2570, 0.2531, 0.2496, and 0.2403, respectively. The small variation in weights across different $\alpha$ settings confirms the robustness of the weighting scheme. Descriptive comparison based on weighted comprehensive scores further shows that genes associated with architectural form, decorative arts, festivals, and food culture tended to perform more strongly, whereas genes related to street-and-lane space, functional zoning, the imperial examination tradition, migration memory, and folk belief performed relatively weakly.

3.3. Comprehensive Benefit Closeness and Benefit-Level Classification

TOPSIS results show that the 17 cultural landscape genes in Dayuwan Village were generally concentrated at medium-to-high benefit levels. Four genes were classified as excellent benefit (${\mathrm{C}}_{\mathrm{j}}$ ≥ 0.75): A111, A112, B131, and B132, accounting for 23.5% of the total and mainly representing Ming–Qing architecture and folk-cultural genes with high participation frequency. Six genes were classified as good benefit (0.70 ≤ ${\mathrm{C}}_{\mathrm{j}}$ < 0.75), accounting for 35.3%, mainly including handicrafts and food culture. Seven genes were classified as medium benefit (0.65 ≤ ${\mathrm{C}}_{\mathrm{j}}$ < 0.70), accounting for 41.2%, including street-and-lane space and the clan system. These medium-benefit genes scored lower on activatability (3.20) and market demand (3.44) than on cultural scarcity (3.89) and cultural identity (4.61), which helps explain their placement in the medium-benefit interval.

3.4. Obstacle Diagnosis Results

The composite obstacle degree ranged from 0.0228 to 0.1975, with a mean of 0.085 and a standard deviation of 0.056, showing a right-skewed tendency. Four genes exhibited high obstacle degrees (${\mathrm{O}}_{\mathrm{i}}$ > 0.15): B111, B121, B141, and B142, accounting for 23.5%; all of them were intangible cultural landscape genes. Four genes exhibited low obstacle degrees (${\mathrm{O}}_{\mathrm{i}}$ < 0.05): A111, A112, B131, and B132, also accounting for 23.5%; these were mainly associated with Ming–Qing architecture and folk-cultural genes with high participation frequency.

In terms of the distribution of constraint mechanisms, the economic transformation constraint mechanism affected 11 genes and was the most frequently observed type in Dayuwan Village, acting mainly on tangible cultural landscape genes. The social identity constraint mechanism affected three genes and was reflected in cultural identity being identified as the dominant obstacle dimension. Composite constraint mechanisms affected four genes, and intangible cultural landscape genes showed a within-case tendency to face the superposition of two or more types of constraints. Among tangible genes, 87.5% were mainly constrained by economic transformation, and their weaknesses were concentrated in activatability (I2), indicating insufficient functional regeneration of physical resources such as buildings and spaces.

Among intangible genes, 22.2% showed a deficit in cultural identity, suggesting that the contemporary translation of living cultural content such as intangible heritage and festivals remains insufficiently developed. Even excellent-benefit genes such as A111 (the Ming–Qing residential building complex) and B131 (intangible heritage skills), although both achieved closeness coefficients above 0.75 (0.782 and 0.775, respectively), still exhibited identifiable constraints, with composite obstacle degrees of 0.041 and 0.026, respectively; specifically, A111 was primarily constrained by activatability, whereas B131 was mainly constrained by cultural identity.

3.5. Supplementary Inferential Checks of the Analytical Expectations

First, using gene-level scores (n = 17), Spearman correlation analysis showed that comprehensive benefit closeness was most strongly associated with market demand (ρ = 0.946, p < 0.001) and activatability (ρ = 0.900, p < 0.001), followed by cultural scarcity (ρ = 0.728, p = 0.001). This pattern supports A1 and suggests that, within the Dayuwan case, higher comprehensive benefit is more closely related to present-day transformability and visitor demand than to historical-symbolic value alone.

Second, composite obstacles appeared only among intangible genes (4/9) and not among tangible genes (0/8). Fisher’s exact test yielded a marginal result (p = 0.053). Although this does not constitute strong statistical confirmation at the conventional 0.05 threshold, it is consistent with A2 and suggests that intangible genes are more likely to face overlapping constraints involving interpretation, identity communication, and demand matching.

Third, among tangible genes, activatability (I2) was the dominant obstacle in 7 of 8 cases (87.5%). Under a conservative equal-probability benchmark across the four criterion-level indicators, an exact binomial test showed that this concentration was unlikely to be random (p < 0.001). This result supports A3 and further indicates that the principal bottleneck for tangible CLGs in Dayuwan lies less in intrinsic cultural value than in adaptive reuse, functional integration, and product translation.

4. Discussion

4.1. Constraint Mechanisms in the Culture–Tourism Transformation of Dayuwan Village

Based on the multidimensional quantitative evaluation and obstacle diagnosis results, the cultural landscape genes of Dayuwan Village display multiple structural shortcomings in the process of culture–tourism integration. Some genes exhibit relatively high composite obstacle degrees under current transformation conditions, indicating that their shift from static conservation to living revitalization is impeded by constraints in the realization of the four value dimensions. To clarify the logical relationship between the intrinsic attributes of cultural landscape genes and the external driving forces that constrain their transformation, this study identified the criterion-level indicators that contributed most strongly to the composite obstacle degree and further summarized them at the mechanism level.

The attribution logic used in this study is as follows: for each gene, the contribution values of the four criterion-level indicators (I1–I4) to the obstacle degree were compared, and the attribute corresponding to the indicator with the largest contribution value was identified as the primary culture–tourism transformation constraint mechanism for that gene. This attribution is used as a heuristic for prioritization rather than as a claim of mutually exclusive causal mechanisms. The “key indicator value” listed in

Table 8. Diagnosis and attribution of constraints in cultural tourism transformation.

Constraint Mechanism Category	Associated Indicator	Core Feature	Key Indicator Threshold	Obstacle Degree Range	Core Impact on Culture–Tourism Integration
Landscape character protection constraints	Cultural scarcity (I1)	Insufficient scarcity; uniqueness diluted	I1 ≤ 3.83	0.0636–0.1972	Loss of regional cultural uniqueness; weakened differentiated competitive advantage
Economic transformation constraints	Activatability (I2)	Low activatability; cultural production function not activated	I2 ≤ 3.32	0.0410–0.1975	Technical/mode bottlenecks in transforming resources into high value-added products
Social identity constraints	Cultural identity (I4)	Insufficient identity; lack of scenario fit	I4 ≤ 4.35	0.0228–0.0296	Reduced recognition of cultural authenticity; fragmented experiences
Market demand constraints	Market demand (I3)	Low demand; weak consumption matching	I3 ≤ 3.54	0.1620–0.1975	Insufficient attraction for target groups; weak motivation for facility investment

marks the low-value threshold used to identify the salience of a given shortcoming.

4.2. Four Major Constraint Mechanisms

(1)

Economic transformation constraint mechanism: constraints in functional integration and adaptive reuse.

The results show that activatability (I2) had a mean value of 3.77 and appeared as the dominant obstacle indicator in 11 genes. Accordingly, the economic transformation constraint mechanism emerged as the most frequently observed bottleneck in Dayuwan Village, covering 64.7% of the evaluated objects. This constraint showed a clear bias toward tangible cultural landscape genes, with 87.5% of such genes affected. This finding suggests that although physical cultural resources possess historical value and cultural scarcity, they still lack effective experiential design, functional integration, and product development in the face of tourism consumption, and thus remain at the stage of static display or symbolic use. For some architectural spaces, transformation pathways remain single and functional complexity is insufficient, meaning that the continuous momentum for adaptive reuse has not yet been established.

(2)

Social identity constraint mechanism: misalignment between cultural narratives and pathways of understanding.

Mechanism-level attribution shows that the social identity constraint mechanism accounted for 17.6% of the sample, covering A141, B131, and B132. Although these three genes fell within the low-obstacle interval overall, their obstacle-factor composition showed a more pronounced relative deviation in cultural identity (I4), reflecting insufficient alignment between “experiential narrative” and “pathways of understanding” and thereby producing a hidden weakness at the identity level. Within current culture–tourism experience systems, cultural narratives often lack a systematic communication logic, and the existing interpretation system is not fully aligned with how visitors understand culture. This mismatch weakens interpretive coherence and makes it harder for visitors to develop recognition and emotional resonance, thereby reducing the identity effect of heritage narratives.

(3)

Landscape conservation constraint mechanism: weakening of regional landscape and original character.

The landscape conservation constraint mechanism mainly appears in the degradation of scarcity and weakening of distinctiveness in some cultural landscape genes. B122 (farming techniques) and B141 (migration memory) are representative examples: their cultural scarcity values were only 3.83 and 3.62, and their obstacle degrees reached 0.0702 and 0.1658, respectively. In some cultural display scenes, the insertion of modern facilities and commercial development has introduced constructions that do not fit the original landscape texture, weakening the integrity and uniqueness of the regional landscape and gradually homogenizing the characteristics of traditional villages. As a result, cultural recognizability and memorable visual points are weakened, affecting visitors’ direct perception of regional landscape character and cultural features.

(4)

Market demand constraint mechanism: mismatch between landscape supply and consumption willingness.

At the criterion level, several genes showed relatively low market-demand scores, indicating uneven consumer recognition across the CLG system. According to Table 7, market demand entered the dominant or composite obstacle structure mainly for B111 (clan system), B121 (imperial examination tradition), and B141 (migration memory), where it interacted with activatability or cultural scarcity rather than operating as an isolated bottleneck. This suggests that the market problem in Dayuwan is not simply a general lack of tourist interest, but a mismatch between existing product translation and visitor-oriented interpretation for historically dense but cognitively demanding genes. At present, culture–tourism products in Dayuwan still rely mainly on sightseeing, farming experiences, and local cuisine, whereas products related to clan institutions, examination culture, and migration memory remain weakly narrated and insufficiently packaged for contemporary audiences. As a result, willingness to pay, dwell time, and revisit motivation are difficult to convert into sustained demand for these genes.

4.3. Revitalization Strategies Under the Five Benefit–Obstacle Scenarios

To balance interpretability, reproducibility, and policy relevance, this study used the comprehensive benefit closeness coefficient and obstacle degree of cultural landscape genes as the two-dimensional diagnostic coordinates and adopted a quantile-threshold, cross-binning classification method to divide revitalization scenarios. Specifically, three breakpoints of 0.65, 0.70, and 0.75 were set on the ${\mathrm{C}}_{\mathrm{j}}$ axis, while 0.05, 0.10, and 0.15 were set on the obstacle degree axis, thereby forming a 4 × 4 objective grid (Figure 7). These breakpoints are case–calibrated management thresholds derived from the Dayuwan sample and were used for intra-case prioritization.

Combined with the actual distribution of the Dayuwan sample, empty sets were removed, and the 17 genes were classified into five revitalization scenario types according to their “benefit–obstacle” combinations: (1) excellent benefit–low obstacle; (2) good benefit–low obstacle; (3) good benefit–medium obstacle; (4) medium benefit–medium obstacle; and (5) medium benefit–high obstacle. On this basis, a scenario-based revitalization pathway for cultural landscape genes was proposed (

Table 9. Activation scenario classification and transformation principles.

Activation Scenario Type	Indicators	Transformation Principle	Specific Genes
Excellent benefit–Low obstacle	${\mathrm{C}}_{\mathrm{j}}$ ≥ 0.75, obstacle < 0.05	Minimal intervention and reversible activation	A111, A112, B131, B132
Medium benefit–Medium obstacle	$0.65 \le {\mathrm{C}}_{\mathrm{j}}$ < 0.70, 0.05 ≤ obstacle < 0.10	Spatial adaptation and composite supply	A121, A122, A123
Good benefit–Medium obstacle	$0.70 \le {\mathrm{C}}_{\mathrm{j}}$ < 0.75, 0.05 ≤ obstacle < 0.10	Collaborative governance and functional optimization	A131, A132, B112, B122, B133
Good benefit–Low obstacle	$0.70\le {\mathrm{C}}_{\mathrm{j}}$ < 0.75, obstacle < 0.05	Adaptive activation and experience upgrading	A141
Medium benefit–High obstacle	$0.65 \le {\mathrm{C}}_{\mathrm{j}}$ < 0.70, obstacle ≥ 0.15	Digital empowerment and restricted development	B111, B121, B141, B142

, Figure 8). To strengthen the governance operability of the five scenarios, this study further links each scenario to a lead actor and a corresponding governance instrument. In the excellent benefit–low obstacle scenario, heritage authorities and the village collective should lead conservation red-line control and the management of permitted-use lists to balance authenticity and limited activation. In the good benefit scenarios, the village collective, local operators, and township government should jointly coordinate adaptive reuse, benefit-sharing, and interpretive upgrading. In the medium benefit–high obstacle scenario, the county-level cultural authority, museum institutions, and academic teams should take the lead in content review, digital interpretation, and restricted development, so as to reduce risks of misrepresentation and over-commercialization (Figure 9).

(1)

Excellent benefit–low obstacle: minimal intervention and reversible revitalization.

This type includes A111 (Ming–Qing residential building complex) and B132 (festivals and rituals). These genes had the highest comprehensive benefit closeness and the lowest transformation resistance and were spatially concentrated in the core conservation area of the village. Development intensity should therefore be restrained, with emphasis placed on minimal intervention and reversible insertion so as to preserve authenticity and the spirit of place. Given their high cultural scarcity (I1 = 4.73) and mild economic transformation constraints, a first step would be to clarify conservation red lines and a permitted-use list and then embed necessary experiential functions through lightweight exhibitions, prefabricated modules, and small-scale performances. For example, the typical patio-courtyard layout under A111 could be transformed into a semi-open cultural experience space without altering the architectural texture, while well-preserved former residential courtyards could host small-scale shadow-play or Huangpi opera performances, allowing visitors to experience intangible heritage dynamically while observing the architectural fabric statically.

(2)

Good benefit–low obstacle: adaptive reuse and experience upgrading.

The representative gene in this type is A141 (traditional handicrafts), mainly corresponding to ceramic kiln sites and farming facilities in the village. Its obstacle degree was extremely low, indicating relatively favorable activation conditions and low overall transformation resistance, but many associated sites are currently abandoned or remain in static display mode. To address this weakness, the priority is to move from superficial observation toward an integrated production–experience–retail chain. Some abandoned kilns could be repaired and pottery-firing skills restored to create a “living pottery workshop”. At the same time, layered products such as workshops, study tours, and markets could be developed and linked to short-video and livestreaming dissemination to form a linked process of promotion, on-site conversion, and repeat visitation. Spatially, a “front shop, rear workshop” model could be adopted: the front area could use the old farming-tool display zone as a cultural-creative retail and ceramic-experience area, while the rear area could retain the authentic firing process as a study-tour education base, thereby attracting families and younger visitor groups to participate deeply and enhancing the participatory and communicative power of handicraft scenes.

(3)

Good benefit–medium obstacle: collaborative governance and functional optimization.

This type includes A131 (mural art), B133 (food culture), and B112 (family precepts and ethos), among others, with comprehensive benefit closeness coefficients between 0.70 and 0.75. These genes are not poor in content supply, but some transformation obstacles remain unresolved, with obstacle degrees around 0.07. The problems are concentrated less in resource absence than in single display modes, broken chains, and insufficient scene complexity. Accordingly, the key is to transform static collections into dynamic experiences through micro-exhibition points and mobile display facilities along streets and lanes, while emphasizing resource integration and composite scene construction. Using Dayuwan’s “seven horizontal and nine vertical” comb-like street system as a linear carrier, a mobile exhibition and interpretation system could be established. Mural art and food culture could be linked as a thematic route, and movable, removable experience and interpretation units, such as craft demonstrations and digital guides, could be placed at street nodes according to mural themes. Through nodal interpretation and route design, dispersed point resources can be integrated into a coherent thematic visitor route, thereby enhancing experiential continuity and reducing efficiency losses caused by resource fragmentation.

(4)

Medium benefit–medium obstacle: spatial adaptation and composite supply.

This strategy targets tangible spatial-pattern genes with medium benefits and medium obstacle degrees, including A121 (feng shui residential pattern), A122 (street-and-lane spatial pattern), and A123 (functional zoning). These genes mainly face economic transformation constraints, with obstacle degrees around 0.084, specifically reflected in low site and route-use efficiency and the absence of composite productive utilization, which prevents the formation of a sustainable culture–tourism revenue chain. Revitalization should therefore proceed from two dimensions simultaneously: spatial functional adaptation and dynamic composite use. For A121, the ecological “pond in front and mountain behind” pattern could be strengthened by adding waterside platforms and night-tour lighting around the village pond, converting a purely scenic water surface into a carrier for the nighttime economy and relieving daytime visitation pressure. For A1222, the “courtyard within courtyard, lane connected to lane” internal space, some underused houses along secondary lanes could be adaptively reused for low-intensity visitor services, such as small-scale accommodation or cultural rest spaces, by taking advantage of the strong privacy of these spaces. At the same time, modern service facilities should be supplemented without damaging the stone-paved street character, thereby addressing the persistent difficulty that traditional lanes face in accommodating contemporary service and consumption needs.

(5)

Medium benefit–high obstacle: digital empowerment and restrictive development.

This type includes B111 (clan system), B121 (imperial examination tradition), B142 (folk belief), and B141 (migration memory). These genes displayed the highest obstacle degrees and were mainly characterized by composite constraints rather than a single shared bottleneck. Specifically, B111 and B121 combined economic transformation and market demand constraints, B141 combined landscape conservation and market demand constraints, and B142 combined landscape conservation and economic transformation constraints. What these genes share is a high cognitive threshold for visitors, a high risk of simplification or misinterpretation, and a weak presence in current experiential scenes, making large-scale physical reconstruction inappropriate. Overall, Dayuwan Museum and the village entrance “Shuikou Garden” may serve as core narrative nodes. A more appropriate pathway is to use narrative digitization and low-disturbance interpretation as the main intervention route. For example, augmented reality could be used in the museum to reconstruct the historical flourish of “four ministers in five generations” under B1211. By scanning selected genealogical exhibits on a mobile phone, visitors could see a dynamic map of the migration of the Yu lineage from Wuyuan, Jiangxi, corresponding to B1411. For the “Three Elders Council”, low-disturbance digital interpretation tools, such as short interactive modules or AR-assisted explanation, could be piloted to make lineage governance more intelligible to younger visitors, thereby lowering the interpretive threshold for visitors through low-cost, low-disturbance means.

4.4. Limitations and Future Research

This study has several limitations that should be noted when interpreting its findings.

First, as a single-case study of a lineage-based village with a relatively intact Ming–Qing built heritage system, the results should be interpreted primarily as case-conditioned rather than directly generalizable to all traditional village types.

Second, although the cultural landscape gene atlas was developed through independent double coding, consensus review, and expert verification, the coding framework remains partly case-tailored and would benefit from further testing in comparative multi-case applications.

Third, the questionnaire used a concise one-item-per-dimension design for each gene in order to maintain diagnostic comparability across the 17 CLGs. Accordingly, the instrument is more suitable for case-based diagnostic evaluation than for full latent-variable measurement.

Fourth, although supplementary inferential tests were added, the analytical unit of this study remains the full set of 17 CLGs within a single village. Accordingly, these tests should be interpreted as within-case hypothesis checks rather than evidence for statistical generalization to the broader population of traditional villages.

Fifth, the weighting coefficient $\alpha$ and the benefit–obstacle scenario thresholds were calibrated for the Dayuwan case and retained on the basis of transparency and robustness checks, but they may require recalibration in villages with different settlement morphologies, heritage compositions, livelihood structures, and tourism-development stages.

Future research could extend the framework to multi-case comparison, strengthen external validation through interviews, behavioral observation, and longitudinal monitoring, and further examine whether the present diagnostic relationships hold across different village types and governance contexts.

5. Conclusions

To address the widespread mismatch between resource value and conversion efficiency in the culture–tourism integration of traditional villages under the national rural revitalization strategy, this study constructed an analytical diagnostic framework integrating “cultural landscape gene atlas construction–comprehensive benefit evaluation–obstacle identification and diagnosis–constraint mechanism attribution–revitalization scenario matching” and demonstrated its analytical usefulness in the Dayuwan case.

Compared with studies focused mainly on static value evaluation or single-dimension satisfaction, this study contributes a diagnostic procedure that links evaluation, obstacle identification, and strategy matching within one case-based analytical sequence. By combining AHP and entropy weighting to determine indicator weights, using TOPSIS to calculate the comprehensive benefit closeness of genes, and introducing an obstacle degree model to identify key bottlenecks, the study achieved a transition from qualitative description to quantitative diagnosis. The framework ranks cultural resources by priority and, more importantly, identifies the mechanisms constraining value conversion, thereby providing a case-based analytical procedure that may inform comparable studies, subject to local recalibration. However, its indicator weights, threshold settings, and scenario allocations should be recalibrated when applied to villages with different settlement morphologies, heritage compositions, livelihood structures, and tourism-development stages.

The Dayuwan case further reveals a case-conditioned pattern of culture–tourism transformation. Supplementary within-case inferential checks further indicate that benefit differences in Dayuwan are driven more strongly by activatability and market demand than by historical-symbolic value alone, while tangible and intangible genes exhibit different modes of constraint accumulation. In this lineage-based village with a relatively intact Ming–Qing built heritage system, tangible cultural landscape genes were more frequently associated with insufficient functional regeneration and scene embedding, whereas intangible cultural landscape genes were more likely, within this case, to be affected by overlapping constraints involving economic transformation, market demand, and/or landscape conservation, depending on the specific gene.

Based on this diagnosis, the five revitalization scenarios proposed in this study range from minimal intervention and authenticity maintenance under excellent benefit–low obstacle conditions to digital empowerment and restrictive development under medium benefit–high obstacle conditions. This scenario-based governance logic provides an alternative to one-size-fits-all development models and suggests multiple pathways for strengthening cultural inheritance, social recognition, and economic use while preserving cultural authenticity.