In this study, the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM) were combined into an EMD-LSTM model, to predict the variation of the >2MeV electron fluxes. Input parameters include the Pc5 power and related geomagnetic indices are used for predictions. As compared the prediction results of the EMD-LSTM model with other classical prediction models, the results show that the one-day ahead prediction efficiency of the > 2MeV electron fluxes possesses a prediction efficiency of 0.80, and the highest prediction efficiency can reach 0.93. These results are superior to the prediction accuracy of more traditional models. Using two high-energy electron flux storm events for validation, the results indicate that the performance of the EMD-LSTM model in the period of the high-energy electron flux storm is also relatively good, especially for the prediction of high-energy electron fluxes at extreme points, and the prediction is closer to actual observation.