Reasonable data mining and accurate prediction and analysis of concrete dam deformation monitoring data are the key means to ensure the safe and long-term operation of the dam. Due to the impact of environmental variables such as temperature and water level, the dam deformation time series has periodic, nonlinear and other change characteristics. Existing intelligent algorithms can not capture the nonlinear relationship of sequences well, A concrete dam deformation prediction model based on EEMD-AEFA-LSTM model was proposed. Ensemble empirical mode decomposition was used to effectively decompose the deformation time series. The long short-term memory network model optimized by the artificial electric field algorithm was used to predict the decomposition components and reconstruct the prediction results. The deformation monitoring data of EX16 and EX24 measuring points of a concrete dam were selected for prediction research. The results show that the prediction accuracy of the EEMD-AEFA-LSTM model is significantly higher than that of the AEFA-LSTM model, PSO-LSTM model, and GA-LSTM model. The average absolute error, mean square error, and root mean square error of the prediction results are the minimum values, providing a new way for accurate prediction of concrete dam deformation.