Abstract: Objectives: The influence of environmental factor classification interval and machine learning model on modeling results in landslide susceptibility evaluation modeling cannot be ignored. In order to explore the influence of these two factors on the evaluation results of landslide susceptibility, Methods: the evaluation index system is constructed by weighting the environmental factors based on the subjective and objective weighting method, and then the influence of different continuous variable factor classification on the accuracy of landslide susceptibility evaluation results is explored by using the GeoDetector. Then, the random forest model, the gradient limit lifting model and the neural network model optimized by genetic algorithm are used to study the landslide susceptibility. Results: The results show that: 1) The maximum AUC value calculated by the partition combination with the highest correlation degree with the disaster obtained by the GeoDetector is 0.886, indicating that the method can obtain the optimal classification interval and can effectively improve the accuracy of the susceptibility evaluation results. 2) In the susceptibility evaluation results, the random forest model is the best, which is 9.7% and 9.6% higher than the gradient limit lifting model and the neural network model optimized by genetic algorithm. Conclusions: The optimal classification interval of environmental factors based on GeoDetector is reasonable, and the random forest model is efficient and accurate as a landslide susceptibility evaluation model.