Abstract: Landslide deformation is the result of the combination of the geological conditions and the external induced factors. The quantitative prediction of landslide deformation is the key to landslide monitoring and early warning. The traditional method based on cumulative displacement-time curve of landslide neglects the influence factors of landslide deformation and evolution, it is difficult to predict landslide deformation accurately. Landslide research in the Three Gorges reservoir area is mostly concentrated on the temporal and spatial distribution characteristics of landslides and the stability analysis of landslides. It is urgent to carry out comprehensive deformation analysis of single landslides. Baishuihe landslide is selected as a case study. Based on landslide macroscopic deformation and displacement monitoring data, spatial-temporal deformation trend of the landslide is analyzed using V/S analysis. Then, long short-term memory neural network model is constructed which consider the influence factors of reservoir water level fluctuation and rainfall hysteresis. It can effectively use the long-term dependent information to realize the quantitative prediction of landslide displacement. The results show that the landslide is characterized by traction landslide, deformation tendency gradually increases from southwest to northeast, and the west and trailing edge are relatively stable. The prediction error is 8.95 mm, which proves the model is of great performance to analyze landslide deformation.