Evaluation of Tourism Development Potential and Its Influencing Mechanisms of Traditional Villages Based on Multi-Source Data and Interpretable Machine Learning: A Case Study of Shexian County, Huangshan City, China

Against the backdrop of China’s vigorous promotion of rural revitalization, traditional villages have become important carriers of rural tourism; however, their tourism development potential varies significantly. Using 182 traditional villages in Shexian County, Anhui Province, as the study area, this paper integrates multi-source data, including remote sensing, socio-economic, and online data. It constructs an evaluation index system from three dimensions: resource endowment, socio-economic conditions, and natural environment. Three machine learning models, namely, Random Forest (RF), XGBoost, and LightGBM, are employed to measure tourism development potential, and the optimal model is selected through comparative analysis. On this basis, the SHAP method is introduced to interpret the influencing factors and reveal the direction and mechanisms of their effects. The results show that (1) the LightGBM model performs best and is more suitable for evaluating tourism development potential of traditional villages; (2) service facilities, land resources, and transportation conditions are the most important influencing factors, while cultural resources and online attention also play significant roles; (3) the effects of different factors exhibit obvious nonlinear characteristics with interaction effects; and (4) the spatial pattern of tourism development potential presents a structure of “core agglomeration–transitional distribution–peripheral dispersion”. From the perspective of multi-source data and explainable machine learning, this study provides a systematic analysis of tourism development potential in traditional villages and offers a scientific reference for their differentiated development and conservation.