黄土丘陵区是地质灾害高发频发的地区之一，亟需采用合适的评价因子和训练模型开展地质灾害易发性评价研究。以郑州“7·20”特大暴雨期间受灾最严重的乡镇巩义市康店镇为研究区，基于卫星遥感解译、实地调查、无人机航拍及相关资料收集，构建覆盖黄土界面、人类工程活动、水动力作用3个主控因素13个影响因子的评价体系，采用CatBoost模型、XGBoost模型和LightGBM模型共3种机器学习算法，开展地质灾害易发性评价研究，基于性能最优的机器学习模型，运用SHAP（shapley additive explanations）算法完成特征全局解释与依赖性分析。结果表明：CatBoost模型的精度高于其他模型（XGBoost和LightGBM），在AUC（area under curve）值、SHAP准确度、精确率、召回率、F₁分数和野外验证中均表现最优，其极高、高、中、低、极低易发区域面积占比分别为3.19%、1.40%、2.04%、5.93%、87.44%，极高、高易发区域主要分布在人类活动强烈的冲沟两侧，切坡建房是地质灾害发生的重要诱因。本次研究旨在优化建模思路，对建模过程的不确定性和可解释性进行研究，对机器学习的易发性决策机理进行解释分析，为豫西黄土丘陵区地质灾害防治提供科学依据。

The loess hilly area is one of the areas with a high incidence of geological disasters, and it is urgent to use appropriate evaluation factors and training models to conduct research on the susceptibility assessment of geological disasters. Kangdian Town, Gongyi City, the township hardest hit during the "7·20" extremely heavy rainstorm in Zhengzhou, was taken as the study area. Based on satellite remote sensing interpretation, field survey, UAV aerial photography and relevant data collection, an evaluation system covering 13 influencing factors of three main control factors, namely loess interface, human engineering activities and hydrodynamic effects, was constructed. CatBoost model, XGBoost model and LightGBM model were used to carry out the evaluation study of geological disaster vulnerability. Based on the machine learning model with the best performance, SHAP(shapley additive explanations) algorithm was used to complete the global interpretation of characteristics and dependency analysis. The results show that the CatBoost model has higher accuracy than other models (XGBoost and LightGBM), and performs the best in AUC(area under curve) value, accuracy, precision, recall, F₁ score, and field validation. The proportion of areas with extremely high, high, medium, low, and extremely low susceptibility is 3.19%, 1.40%, 2.04%, 5.93%, and 87.44%, respectively. The extremely high and high susceptibility areas are mainly distributed on both sides of gullies with strong human activities, and slope cutting and building are important causes of geological disasters. The aim of this study is to optimize the modeling approach, investigate the uncertainty and interpretability of the modeling process, explain and analyze the decision-making mechanism of machine learning susceptibility, and provide scientific basis for geological disaster prevention and control in the loess hilly area of western Henan.