Dam monitoring data is an important guarantee of dam safety. Anomaly detection and recovery of dam monitoring data can effectively avoid the wrong estimation and judgment of dam status, which has important practical significance. In recent years, there are extensive studies on anomaly detection of dam monitoring data based on deep learning methods. However, the existing methods have some drawbacks such as insufficient data utilization and insufficient information mining. Therefore, a multi-dimensional LSTM anomaly detection and recovery method is proposed in this paper. The dam monitoring data of multiple monitoring points are fed into LSTM to predict the data of single monitoring point, and the relevant information between different monitoring points is effectively utilized. Finally, anomaly detection is performed on the data of the target detection point using Pauta criterion. In this paper, the laser collimation monitoring data of Fudougou Hydropower Station in Dadu River are used for case verification. By comparing with the single dimension LSTM anomaly detection and recovery algorithm, it is verified that the performance of proposed method is effective both in anomaly detection and data recovery, which is an effective method for dam monitoring data anomaly detection and recovery.