Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data.