Abstract: This paper takes Lingtai County, Pingliang City of Gansu Province as target research area. Based on the geospatial and historical landslide data, four machine learning models were used to construct the landslide susceptibility evaluation model. The four models are BP neural networks model, Random Forest classification model, support vector machine model, and logistic regression model which were optimized by GMM cluster model. In this paper, seven factors are selected as the landslide susceptibility influence factors, including elevation, slope, aspect, loess erosion intensity, vegetation coverage and geological structure. The influence factor of the geospatial database is established with 30m grid. The target area is divided into 1.8 million grid cells, and the grid cells of the whole area are clustered by the GMM model to obtain the preliminary subarea of landslide susceptibility map. 500 grid cells in the lowest-susceptibility category are selected as non-landslide units randomly, and 203 landslide grid units were used as landslide units according to historical landslide data. trained model is used to simulate and predict the whole research area, and to draw the ROC curve of each algorithm. Then compare the prediction results of each algorithm. The results of the analysis showed that the landslide susceptibility map of each algorithm is consistent with the actual landslide development. The random forest model has the largest area of 0.96 under the ROC curve, and the highest prediction accuracy of 0.93. It is followed by the BP-neural-network model with 0.89 under the ROC curve and 0.87 of the prediction accuracy. The area under the ROC curve and prediction accuracy of the support-vector-machine-model is 0.86, 0.81; and the logistic regression model is and 0.85, 0.80 respectively. The latter are lower than the first two models.