Abstract. Debris flow, landslide, ground collapse, collapse and ground collapse are the dominating geological hazards in Bijie city, Guizhou province, which is situated in the area with high natural hazards in China. The primary purpose of this study is to construct different classification models by using the disaster conditioning factors of geological hazards and to evaluate the performance of the models in the classification of geological hazards in Bijie city. At the same time, the nonlinear relationship between various geological hazards and conditioning factors will be discussed. Firstly, the manual field survey data of Bijie city in 2019 were applied to construct and draw inventory map of six geological hazards. Then 16 conditioning factors were established from various data sources. According to the ratio of 70:30, the geological hazard location points were randomly divided into the training and validation set to complete the training and verification process of the classification models. In order to select the optimal subset of the conditioning factors, the multicollinearity of these factors was assessed using tolerances and variance inflation factors(VIF) and Pearson’s correlation coefficient, and factors with multicollinearity were excluded to optimize the model. Subsequently, ten classification models were structured, and the models were verified and compared by using the receiver operating characteristic(ROC), precision, sensitivity, Kappa coefficient and F1 values.In addition, the Friedman test was used to identify statistically significant differences between the results of the classification model used in this research. In general, average Area Under Curve (AUC) values under the ROC curves of the 10 classification models is above 0.8, indicating that all models have a corresponding high prediction ability. Among them, the average AUC value(0.941), AUC values for individual geological hazards (collapse: 0.949, ground crack: 0.907, ground collapse: 0.952, landslide: 0.830, displacement flow: 0.963, slope: 0.922), Kappa coefficient (0.845), Macro F1(0.851) and Micro F1(0.878) of SVM all had the highest values.