Slope stability prediction research is a complex non-linear system problem. In carrying out slope stability prediction work, it often encounters low accuracy of prediction models and blind data preprocessing. Based on 77 field cases, 5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability. These indicators include slope angle, slope height, internal friction angle, cohesion and unit weight of rock and soil. Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods, namely principal components analysis (PCA), Kernel PCA, factor analysis (FA), independent component analysis (ICA), non-negative matrix factorization (NMF) and t-SNE (stochastic neighbor embedding). Combined with classic machine learning methods, 7 prediction models for slope stability are established and their reliabilities are examined by random cross validation. Besides, the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method. The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability. Random forest (RF), support vector machine (SVM) and k-nearest neighbour (KNN) achieve the best prediction accuracy, which is higher than 90%. The decision tree (DT) has better accuracy which is 86%. The most important factor influencing slope stability is slope height, while unit weight of rock and soil is the least significant. RF and SVM models have the best accuracy and superiority in slope stability prediction. The results provide a new approach toward slope stability prediction in geotechnical engineering.

边坡稳定性预测研究是一个复杂的非线性系统问题。在开展边坡稳定性预测工作中，经常遇到预测模型精度低、数据预处理盲目等问题。基于77个现场案例，筛选出5个定量指标，提高边坡稳定性预测模型的准确性。这些指标包括坡角、坡高、内摩擦角、岩土的黏聚力和单位重量。基于六维约简方法，即主成分分析（PCA）、核PCA、因子分析（FA）、独立成分分析（ICA）、非负矩阵分解，对边坡稳定性预测中的潜在数据聚合进行分析和可视化(NMF) 和t-SNE（随机邻居嵌入）。结合经典的机器学习方法，建立了7个边坡稳定性预测模型，并通过随机交叉验证检验了它们的可靠性。此外，利用变异系数法讨论了各指标在边坡稳定性预测中的意义。研究结果表明，本文建立的边坡稳定性预测模型的数据处理不需要降维。随机森林（RF）、支持向量机（SVM）和k近邻（KNN）的预测精度最好，高于90%。决策树 (DT) 具有更好的准确率，为 86%。影响边坡稳定性最重要的因素是边坡高度，而岩土单位重量的影响最小。RF和SVM模型在边坡稳定性预测方面具有最好的精度和优势。研究结果为岩土工程中的边坡稳定性预测提供了一种新方法。