Abstract: To further improve the accuracy of landslide hazard prediction models and enhance their interpretability, this study selected 8 influencing factors of landslide occurrence, taking the Yili River Basin, Xinjiang province as an example. An interpretable BPNN-SHAP model, based on the back propagation neural network (BPNN) model and the game theory with the aim of addressing the 'black box' issue, was constructed. Firstly, the dataset was divided into 70% training set and 30% test set, and 5-fold cross-validation was used to enhance the robustness of the BPNN-SHAP model. Then, the evaluation accuracy of this model was compared with three other models: Deep Neural Network (DNN), Random Forest (RF), and Logistic Regression (LR). Finally, regional landslide hazard assessment was completed, and the interpretability of BPNN-SHAP was also discussed. The results showed that the BPNN-SHAP model achieved the highest statistical values in the following metrics: Accuracy (A)=0.904, Precision (P)=0.911, Recall (R)=0.919, F1_Score=0.915, and S_AUC=0.905. The very high and high danger areas for landslides in the study region accounted for 11.96% and 15.53%, respectively. Among these regions, Xinyuan and Nileke County occupy the highest proportions, at approximately 51.1% and 45.6%, respectively. The primary controlling factors for landslides were elevation, slope, rainfall, and peak ground acceleration (PGA). Specifically, areas with an elevation of 1500 m to 2000 m, slopes greater than 14°, annual rainfall between 260 mm and 310 mm, and PGA greater than 0.23 g are prone to landslides, indicating that the predominant types of landslides are rainfall-induced and earthquake-induced. Our research method is expected to provide a new technical reference for landslide hazard assessment and theoretical support for disaster prevention, mitigation, and resilience construction in the Yili River Basin.