Abstract: To enhance the precision of landslide hazard evaluations and elucidate the assessment process of landslide risks influenced by engineering activities, this study introduces a DNN-SHAP interpretable neural network model using the area along the China-Pakistan Highway as a case study. Initially, 12 risk assessment factors such as the distance to the road and slope gradient were selected. The Pearson correlation coefficients among these factors were calculated to exclude highly correlated ones. Subsequently, a DNN(Deep Neural Network) model was developed for landslide prediction and comprehensively compared with Random Forest(RF), Support Vector Machine(SVM), and Logistic Regression(LR)models. Finally, the SHAP(SHapley Additive exPlanations) model was utilized to determine the contributory influence of each factor in the DNN prediction process. This completes the evaluation of landslide hazards due to engineering disturbances and explains the dependent coupling relationships between influencing factors. The research outcomes demonstrate that, except for the Precision metric where it is slightly outperformed by the SVM model, the proposed DNN-SHAP model surpasses the other three models in prediction accuracy. Moreover, the method quantitatively reveals that the synergistic effects of the distance to the road, lithology, and topographic relief are the dominant factors in controlling the engineering-disturbed landslide hazards in this region. This provides new method and technical references for the evaluation of landslide hazard risks.