Abstract: The interaction between environmental factors, meteorological factors, and human activities affects surface morphology change. Especially for the Loess Plateau region, it is easy to cause a loess slide disaster under the complex interaction of many factors, so selecting suitable influencing factors and training models to conduct landslide susceptibility evaluation research is urgent. This study takes Tianshui City as the research area and constructs a multi-factor evaluation system covering terrain scale, basic environmental factors, and human activity scale based on the surface deformation information obtained by InSAR. The coupled models IV-RF, IV-DT, IV-SVM, and IV-BP were constructed by connecting the information content model (IV) to the random forest model (RF), decision tree model (DT), support vector machine model (SVM) and BP neural network model (BP), and the landslide susceptibility evaluation was carried out. The results show that the AUC values of the coupled models (IV-RF, IV-DT, IV-SVM, and IV-BP) are 0.925, 0.846, 0.883, and 0.792, respectively, and IV-RF has stronger accuracy. Compared with the IV-RF model, the landslide frequency gradually increases from the very low prone zone to the very high prone zone, and the results of the landslide-prone zone are more uniform and stable. The IV-RF model has stronger prediction ability and accuracy and is more suitable for evaluating the geological hazard susceptibility of loess landslides. The areas of extremely high, high, medium, low, and very low susceptibility in the IV-RF model accounted for 20.45%, 18.28%, 22.27%, 16.92 and 22.09%, respectively, which were mainly distributed in the mountainous and loess ridge areas with complex geological environment and strong human activities in the north of Tianshui City. Lithology, slope, land use, rainfall, road density, and InSAR deformation rank the top 6 in the contribution rate analysis and are the main controlling factors affecting landslide development. This study aims to provide a reliable scientific basis for predicting and preventing landslide disasters in the Loess Plateau, deepen the modeling ideas for evaluating landslide susceptibility, and optimize the uncertainty of independent model evaluation results.