The selection of different landslide susceptibility evaluation models will result in differentiated spatial prediction results, and selecting the optimal model is an effective method to improve the accuracy of landslide spatial prediction. Taking the Chaya County landslide in Tibet as the research object, 8 evaluation factors from 3 categories were selected. Based on the single model evaluation using certainty factor(CF), frequency ratio (FR), information value (I), and weight-of-evidence(WoE), the evaluation results of the four single models were fused using normalization fusion and principal component analysis (PCA) fusion methods to obtain 6 landslide susceptibility zoning maps in the study area. The research results show that the frequency ratio values of the 6 models show a significant increasing trend from low susceptibility areas to extremely high susceptibility areas. The sum of frequency ratio values in extremely high and high susceptibility level zones exceeds 97% of the total frequency ratio value, effectively evaluating the susceptibility of landslides in the study area. By constructing receiver operating characteristics(ROC) curves to analyze the effectiveness of the evaluation models, it is shown that the PCA fusion model, evidence weight model, and information value model have high area under curve(AUC). Overall, the PCA fusion model performs better when considering the results of frequency ratio in high and extremely high susceptibility zones. The study fills a gap in the comparative study of multiple models in the Tibetan Plateau mountainous area and provides a scientific basis for landslide prediction and prevention in the region.