滑坡灾害对居民生活和社会经济构成严重威胁。以新源县为研究区，选取17个滑坡影响因子作为初始因子集，通过多重共线性分析，筛选10个滑坡因子并构建研究区滑坡易发性评估指标体系。分别基于逻辑回归（logistic regression, LR）、支持向量机（support vector machine, SVM）和随机森林（random forest, RF）3种典型模型进行滑坡易发性评估。利用受试者操作特征（receiver operating characteristics, ROC）曲线下的面积（area under curve, AUC）、滑坡比及野外实地考察对模型评估结果进行对比验证分析。结果显示：低易发区主要集中在巩乃斯河谷平原地区，这些区域地势平坦，滑坡易发性相对较低。高易发区主要集中于巩乃斯河谷北部、阿吾拉勒山丘陵区，以及南部伊什基里克山和那拉提山分水岭两侧、恰甫河以南区域，地形复杂多变，滑坡易发性较高。在3种评估模型中，SVM模型表现最佳，其AUC高达0.985，说明该模型在滑坡易发性评估中的准确性较高。此外，SVM模型评估得到的高易发区滑坡点密度大，占比达到86%，进一步验证了其在滑坡易发性评估中的有效性。综合以上结果，SVM模型在新源县滑坡易发性评估中的合理性优于其他两种算法，能够为该地区的滑坡防治提供科学的理论依据和参考。

Landslide disasters pose a serious threat to residents' lives and socio-economic development. Taking Xinyuan County as the study area, 17 landslide influencing factors were selected as the initial factor set. Through multiple collinearity analysis, 10 landslide factors were screened and an evaluation index system for landslide susceptibility in the study area was constructed. The landslide susceptibility was then evaluated based on three typical models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The evaluation results of the model were compared and validated using the area under curve (AUC), landslide ratio, and field investigation under the receiver operating characteristics(ROC) curve. The results show that low-susceptibility areas are mainly concentrated in the valley plain of the Kongnais River, where the terrain is flat and the landslide susceptibility is relatively low. High-susceptibility areas are mainly located in the northern part of the Kongnais River valley, the Awulale hilly area, and the watersheds on both sides of the southern Yishikelike Mountains and Nalati Mountains, as well as the area south of the Qiafu River, where the terrain is complex and varied, leading to a higher susceptibility to landslides. Among the three evaluation models, the SVM model performs the best, with an AUC value of up to 0.985, indicating its high accuracy in landslide susceptibility assessment. Furthermore, the high-susceptibility areas identified by the SVM model have a high density of landslide points, accounting for 86% of the total, further validating its effectiveness in landslide susceptibility assessment. Based on the above results, the SVM model is more reasonable than the other two alrorithms in assessing landslide susceptibility in Xinyuan County, providing a scientific theoretical basis and reference for landslide prevention and control in the region.