[Objective] This study aims to establish a technical pathway of ‘factor selection-model evaluation-mechanism analysis’ to investigate evaluation models with high predictive accuracy for landslide susceptibility， reveal the key driving factors of landslide disasters， and explore the interaction mechanisms among landslide influencing factors under complex geological conditions. It can provide scientific support for disaster risk management and the formulation of ecological protection strategies in the Jiuzhaigou scenic area and similar post-seismic regions with highly concealed landslides. [Methods] The Jiuzhaigou scenic area was selected as the study area. Landslide susceptibility was evaluated using both traditional methods-analytic hierarchy process （AHP）， information value （IV）， and certainty factor （CF） and machine learning models （XGBoost， LightGBM， and CatBoost）. A systematic evaluation indicator system was constructed based on correlation analysis and collinearity tests. The shapley additive explanations（SHAP） explainable algorithm and the optimal parameter-based geodetector model （OPGD） were used to identify key controlling factors and investigate their interaction mechanisms. [Results] Among the landslide susceptibility evaluation models， the machine learning models overall outperformed the traditional methods， with the CatBoost model achieving the highest predictive accuracy （AUC=0.927）. High-susceptibility zones were concentrated in Panda Lake， Arrow Bamboo Lake， northwestern Danzugou， southwestern Grass Lake， and southeastern Long Lake. Both SHAP and OPGD identified distance to water systems， normalized difference vegetation index （NDVI）， slope aspect， and multi-year average annual rainfall as the primary controlling factors. OPGD interaction detection revealed that the interaction between distance to water systems and distance to faults was the strongest （q=0.33）， and the relationship between multi-year average annual rainfall and NDVI showed a nonlinear enhancement （q=0.16）. [Conclusion] There are multiple potential zones of high landslide susceptibility within the Jiuzhaigou scenic area. Based on high-accuracy evaluation models， the SHAP algorithm effectively identifies key driving factors. Moreover， the synergistic effects of multiple factors are the key mechanisms for landslide development in this region.