Abstract: In order to comprehensively consider the differences between different types of landslides, improve the accuracy and engineering application value of landslide susceptibility prediction, taking Wanzhou District, Chongqing as an example, support vector machine, C5.0 decision tree, logistic regression, and multilayer perceptron models were applied to model the susceptibility of a single stacked layer landslide and rockfall. Based on the optimal comprehensive performance model, direct coupling method, probability statistics method, and susceptibility comparison method were used to couple and model different types of landslides, and their uncertainties were evaluated. The modeling results show that the C5.0 decision tree model performs best in susceptibility prediction for single-type landslides, with AUC values exceeding 0.930. The susceptibility prediction results of the three coupling methods are generally consistent with the actual situation, among which the comparative susceptibility method exhibits higher AUC values and accuracy in frequency ratio, demonstrating the best predictive performance. The probabilistic statistical method ranks second, followed by the direct coupling method. In terms of comprehensively reflecting the evolution patterns of landslides, adapting to actual scenarios, and supporting prevention and control decisions, considering the coupling of landslide types yields better landslide susceptibility predictions than considering only single-type landslides. However, it is still necessary to explore the specificity among different types of landslides and optimize coupling methods in greater depth in the future.