Deformation monitoring and prediction are crucial for early warning and timely remedial measures for abnormal situations in hydroelectric power stations. In this study, a Long Short-Term Memory (LSTM) neural network method was proposed to predict the time series deformation of the Pubugou Waterfall Power Station in the Dadu River Basin using Interferometric Synthetic Aperture Radar (InSAR) data. First, the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique was used to monitor the time series deformation of the Pubugou Waterfall Power Station based on Sentinel-1 images from 2018 to 2020. Then, an LSTM neural network was employed to establish a deformation prediction model based on the time series InSAR deformation data. The results show that the maximum subsidence rate of the Pubugou Waterfall Power Station reaches -34mm/a-1. The point-scale RMSE and MAE of the LSTM prediction model during training and testing are the minimum values of 2.343mm and 2.010mm, and 2.094mm and 1.654mm, respectively. The predicted results of the LSTM deformation prediction model indicate that the cumulative subsidence from May to September 2020 will reach 71.29mm. This study demonstrates that the LSTM neural network is an effective method for predicting InSAR time series deformation. Moreover, the predicted results of this model can also be used for deformation warning and decision-making support for the Pubugou Waterfall Power Station.