To improve the prediction accuracy of concrete dam deformation monitoring data, a long and short-term memory network (LSTM) prediction model was constructed based on integrated empirical mode decomposition (EEMD) and sample entropy reconstruction (SE). In this model, EEMD is firstly used to decompose the original data sequence, and the sample entropy of each component sequence is then calculated. The sample entropy of the original sequence is used as the reference for reconstruction, and then the LSTM model is established to predict the reconstructed sequences. Finally, the predicted values are superimposed to obtain the final prediction result. Taking a concrete arch dam as an example, the prediction results of the EEMD-SE-LSTM model were compared with those of EMD-LSTM, LSTM and SVM models. The results show that the EEMD-SE-LSTM model has higher prediction accuracy and better feasibility and superiority in the prediction of concrete dam deformation.