Machine learning models are widely used for landslide susceptibility evaluation due to their powerful feature extraction ability, and their algorithms are constantly improving in application. To address the issue of low accuracy in traditional machine learning models, this paper introduces the group convolutional neural network (GCNN) model into landslide susceptibility evaluation, and compares and analyzes the results with various traditional machine learning models to comprehensively evaluate the applicability of the GCNN model in landslide susceptibility evaluation.Taking Hebei Province as the study area, from three aspects of disaster causing factors, disaster pregnant environment and disaster bearing body, a total of 18 influencing factors were selected, GCNN and the current mainstream machine learning models - convolutional neural network model (CNN), logical regression model (Logistic), random forest algorithm model (RF) and support vector machine model (SVM) were selected to build the corresponding vulnerability assessment model, divide the research area into five types of landslide prone areas, and comprehensively evaluate the accuracy of the zoning.Research has shown that compared with the other four machine learning models, GCNN has higher scores under various confusion matrix indicators and is more suitable for landslide susceptibility zoning; The results of landslide prone zoning obtained by GCNN are consistent with the actual landslide occurrence points, and the landslide prone areas classified are more accurate.