Abstract: At present, the traditional means of monitoring the surface deformation of landslides are still limited by the small monitoring area, the difficulty of obtaining information in complex terrain, and the high economic cost. The non-linearity and uncertainty of the deformation time series of large and complex landslides are difficult to solve in landslide deformation monitoring and forecasting. In this study, For the Fanjiaping landslide in the Three Gorges Reservoir area, The Small BAseline Subset InSAR (SBAS-InSAR) is used to monitor the landslide deformation in combination with the surface GPS monitoring data, and the landslide deformation prediction is carried out based on the SBAS-InSAR time-series data and Long Short-Term Memory (LSTM) network. The results indicate that during the study period, the deformation area and deformation magnitude monitored by SBAS-InSAR are basically consistent with that from surface GPS monitoring and field investigation. The displacement and deformation of the Fanjiaping landslide at different elevations are not always affected by the buoyancy reduction. When the reservoir water level is below approximately 160 m, seepage pressure dominates the deformation. The overall displacement and deformation during the water level decrease period are significantly greater than during the water level rise period. The decrease rate in reservoir water level has a significant impact on the displacement and deformation of the Fanjiaping landslide. Additionally, the Muyubao landslide area shows a stronger deformation response to the decrease rate of reservoir water level compared to the Tanjiahe landslide area. (Fanjiaping landslide is further divided into Muyu Bao landslide area and Tanjiahe landslide area based on the direction of landslide creep and deformation response). Multiple methods, including a comparison with traditional methods, confidence interval estimation, and correlation tests, were conducted to evaluate the prediction results of the LSTM model. It demonstrates that the LSTM model always has high precision and reliability. This study can provide important data and information support for the prevention and control of geological disasters in the Three Gorges reservoir area.