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Article

Inter-Provincial Similarities and Differences in Image Perception of High-Quality Tourism Destinations in China

1
Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Architecture, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(10), 1999; https://doi.org/10.3390/land14101999
Submission received: 9 September 2025 / Revised: 28 September 2025 / Accepted: 3 October 2025 / Published: 5 October 2025

Abstract

With the rapid development of China’s tourism industry, the homogenization of regional tourism images has become a growing concern. To address this, this study quantifies the similarities and differences in tourism image perception across China’s 31 provinces, focusing on 350 5A-level destinations, analyzing 757,046 tourist reviews collected from Ctrip.com in 2024. Using a three-dimensional framework (cognitive, affective, and overall image), we analyze social media data through natural language processing, random forest regression, and social network analysis. Key findings include the following: (1) most comments are positive, with Jiangsu and Chongqing showing high cognitive image similarity but low overall similarity; (2) cognitive image significantly impacts affective image, especially through unique tourism resources; (3) an inter-provincial similarity–difference matrix reveals significant perceptual differences among provinces. This study provides a novel methodological approach for multidimensional image evaluation and offers crucial empirical insights for regional policy-making aimed at optimizing land and tourism resource allocation, balancing regional disparities, and promoting sustainable land use and development across China.
Keywords: tourism image perception; high-quality tourism destinations; big data analysis; random forest regression; social network analysis; inter-provincial; Python tourism image perception; high-quality tourism destinations; big data analysis; random forest regression; social network analysis; inter-provincial; Python

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MDPI and ACS Style

Zhao, W.; Liu, J.; Zhu, H.; Li, F.; Zhu, Z.; Zhengchen, R. Inter-Provincial Similarities and Differences in Image Perception of High-Quality Tourism Destinations in China. Land 2025, 14, 1999. https://doi.org/10.3390/land14101999

AMA Style

Zhao W, Liu J, Zhu H, Li F, Zhu Z, Zhengchen R. Inter-Provincial Similarities and Differences in Image Perception of High-Quality Tourism Destinations in China. Land. 2025; 14(10):1999. https://doi.org/10.3390/land14101999

Chicago/Turabian Style

Zhao, Wudong, Jiaming Liu, He Zhu, Fengjiao Li, Zehui Zhu, and Rouyu Zhengchen. 2025. "Inter-Provincial Similarities and Differences in Image Perception of High-Quality Tourism Destinations in China" Land 14, no. 10: 1999. https://doi.org/10.3390/land14101999

APA Style

Zhao, W., Liu, J., Zhu, H., Li, F., Zhu, Z., & Zhengchen, R. (2025). Inter-Provincial Similarities and Differences in Image Perception of High-Quality Tourism Destinations in China. Land, 14(10), 1999. https://doi.org/10.3390/land14101999

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