Bayesian Network and SBAS-InSAR have been widely used in landslide susceptibility evaluation. However, how to give full play to the advantages of the two methods and integrate the two methods to further improve landslide susceptibility evaluation remains to be explored. Therefore, this paper takes Hanyuan County, where landslides frequently occur, as the study area. According to topographic and meteorological data, 20 traditional landslide factors are extracted, and according to SAR satellite data, the surface deformation rate is extracted using SBAS-InSAR technology as a deformation factor to participate in the calculation. After using the GeoDetector to screen the dominant factors, the Bayesian Network structure is constructed using the hill climbing algorithm combined with expert knowledge in the case of deformation factors, removal of deformation factors, and supplementation of one factor. The landslide susceptibility calculation is performed after parameter learning using the maximum likelihood estimation algorithm. The results show that the AUC value of the Bayesian Network model with deformation factors added reaches 0.957, which is 8.63% and 8.26% higher than that of the Bayesian Network without deformation factors and supplementation of one factor, respectively, and the susceptibility zoning results are more reasonable. The landslide susceptibility evaluation method provided in this paper can provide guidance for regional disaster prevention and mitigation, and provide new ideas for related research.