Understanding Regional and Stylistic Diversity in Chinese Rural Paper-Cutting Through Convolutional Neural Network-Based Image Classification

As an important component of Chinese folk art, rural paper-cutting embodies rich regional cultural connotations and distinctive aesthetic expressions. In this study, a Chinese rural paper-cutting image dataset covering multiple regions and artistic styles was constructed, and a convolutional neural network (CNN)-based framework was proposed for regional and stylistic identification of paper-cutting works. Five representative mainstream CNN models were evaluated for both tasks. For regional classification, all models achieved high accuracy, with EfficientNet-B1 attaining the highest accuracy of 91.46%. The style classification task was more challenging due to subtle visual differences, with MobileNetV3-Small achieving the highest accuracy of 73.20%. In addition, t-distributed stochastic neighbor embedding (t-SNE) visualizations further confirmed that the models were able to effectively distinguish different regional and stylistic categories in high-dimensional space. To enhance model interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to visualize the optimal models. The results show that the CNNs consistently focus on core structural features of paper-cutting works, suggesting that CNNs can capture visually and culturally meaningful features. Overall, this study demonstrates the feasibility of applying CNNs to the analysis of traditional folk art and provides a practical technical pathway for digital management, intelligent classification, and educational dissemination of rural paper-cutting art.