Abstract: Accurate landslide susceptibility maps are beneficial for management departments to carry out land use planning and disaster prevention and mitigation. It has been an important field in the landslide risk assessment and management in China. This study aims to compare and analyze the performance of different data-driven models in the assessment of regional landslide susceptibility. The middle reaches of the Yellow river were selected as the study area, and a database including 684 historical landslide points was obtained through detailed field investigation combined with visual interpretation of remote sensing images. 14 evaluation factors were selected, Pearson correlation coefficient was used to analyze the correlation between these factors, and the C5.0 decision tree algorithm was used to determine the importance of each factor. Three typical data-driven models (Weighted Information Volume (WIV), Support Vector Machine (SVM) and Random Forest (RF)) were selected to evaluate the regional landslide susceptibility, and the performance of the models were verified by the Receiver Operating Characteristic (ROC) curve and the area AUC value under the curve. The results show that the distance from the road, the distance from the river and the slope are the most important contributing factors to the occurrence of landslides in this area. The majority of historical landslides occurred in the moderate and high susceptibility zones on the landslide susceptibility map. The landslide points in the high/very high susceptibility area obtained by SVM and RF models exceed 70% of the total landslide points. The RF model performed the best, with the high susceptibility area accounting for 21.9% of the area and the number of landslides accounting for 90.5% of all historical landslide points. A comparison of AUC accuracy shows that the RF model is more accurate than the other two models: RF has an AUC of 0.904, while WIV and SVM have AUCs of 0.845 and 0.847 respectively.