Landslide susceptibility assessment is a crucial tool for proactively preventing and controlling landslide disasters and avoiding casualties and property damage. This letter proposes a dynamic method for calculating landslide susceptibility， which aims to quantify the influence of the rainfall as the main inducing factor， on the landslide deformation using the time–frequency analysis method， allowing the calculation of landslide susceptibility on a time scale. Firstly， the rainfall and the surface deformation data from Interferometric Synthetic Aperture Radar （InSAR） are performed by the wavelet analysis to quantify the response of surface deformation to rainfall. Secondly， the rainfall after calculating the quantitative relationship and other landslide elements are fed into the designed Random Forest model （RF）. Finally， the landslide sensitivity is compared across time to assess the timeliness of the model. The results demonstrate that the landslide sensitivity model can effectively predict the landslide risk changes and high predictive accuracy. The AUC is 0.962， with accuracy and precision rates of 0.916 and 0.941. Furthermore， the model presented demonstrates significant discriminative power in time series analysis. and high-risk areas primarily concentrated around the known landslide hazards， suggesting that the predictions of the model coincide with the distribution of actual landslide hazards.