Abstract: The competitiveness of rural ecological tourism resources is an important basis for judging the potential for tourism development and is of great significance for the revitalization of rural industries.XGBoost-SHAP interpretable machine learning provides a visual intelligent tool for measuring the competitiveness of rural ecological tourism resources. In this study, XGBoost-SHAP was integrated to construct an interpretable machine learning model with rural ecotourism resource sites as sample data. Measurement indicators were selected from three dimensions: natural and humanistic environment, ecological resources and tourism infrastructure, in order to identify the competitiveness level of rural ecotourism resources in Chongqing Municipality. The results indicated that: (1) XGBoost, by learning the potential patterns or laws of the sample data, efficiently identified the different competitiveness levels of the rural ecotourism sites and achieved a high accuracy. (2) SHAP improved the transparency of XGBoost, model prediction and was able to identify the dominant factors of competitiveness of rural ecotourism resources. Economic activity intensity, NDVI, elevation and habitat quality were the four most important factors for the competitiveness of rural ecotourism resources in Chongqing Municipality, and they were also the main cooking factors for the development and sustainable utilization of rural ecotourism resources. (3) In Chongqing Municipality, the strong competitiveness of rural ecotourism resources was concentrated in the market, well-known scenic spots and along the transportation route, forming three rural ecotourism development modes: market-dependent in the west of Chongqing Municipality, transportation-dependent in the southeast and northeast, and scenic spot-dependent. (4) There was a spatial spillover effect on the competitiveness of resources, and the proximity to the core scenic area was the main factor affecting its strength. The human conditions such as tourism infrastructure and market also had an important impact. Based on the research findings, suggestions were provided for balancing the development and utilization of tourism resources in Chongqing, optimizing the spatial layout of tourism, and promoting the construction of "beautiful villages." Interpretable machine learning models were proved to be able to quickly and accurately measure the competitiveness of rural ecological tourism resources in the region, which provided a methodological reference for identifying and quantitatively analyzing tourism resources.