为对边坡稳定性进行广义高效地准确判断，引入了一种新型的CatBoost集成学习算法评价边坡稳定性。根据边坡基本几何与地质要素，无需数值建模与计算，直接客观地评判边坡稳定性状态，并从概率信息论角度给出边坡稳定性概率，实现速度快、精度高、稳健性好的广域尺度下边坡稳定性评价。以影响边坡稳定性的5个主要因素作为评价指标，创造了大型边坡稳定性评价数据集，据此构建了基于CatBoost集成学习的边坡稳定性预测模型。仿真结果表明，对比常见机器学习和代表性集成学习模型，CatBoost模型预测效果明显优于其他模型，且减少了超参数调优需求，更具有通用性、客观性和可靠性，可有效应用于边坡稳定性初步评价。通过举例仁化（湘粤界）至博罗高速公路仁化至新丰段某边坡工程，验证了基于CatBoost集成学习的边坡稳定性评价方法的可行性。

In order to accurately judge slope stability in a generalized and efficient sense, a new CatBoost ensemble learning algorithm was introduced to evaluate slope stability. According to the basic geometry and geological elements of the slope, without the need for numerical modeling and calculation, the stability state of the slope is directly and objectively evaluated, and the probability of slope stability was given from the perspective of probability information theory, so as to realize the stability evaluation of the slope at the wide-area scale with fast speed, high precision and good robustness. Taking five main factors affecting slope stability as evaluation indexes, a large slope stability evaluation dataset was created, and a slope stability prediction model based on CatBoost ensemble learning was constructed. The simulation results show that compared with common machine learning and representative ensemble learning models, the prediction effect of CatBoost model is significantly better than that of other models, and the hyperparameter tuning requirements are reduced, which is more versatile, objective and reliable, and can be effectively applied to the preliminary evaluation of slope stability. Through the example of a slope project of Renhua to Xinfeng section of Renhua (the junction of Hunan and Guangdong Province) to Boluo Expressway, the feasibility of slope stability evaluation method based on CatBoost ensemble learning is verified.