The LSTM（long short-term memory）has the problems of long running time and dimension disaster in processing large data sets.In order to solve the problems,an intelligent fault diagnosis model based on energy entropy and CL-LSTM (long short term memory network with center loss) was proposed.The original signal was decomposed into several components with complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）adaptively;IMF components were selected through correlation coefficients，and its energy entropy was calculated as a new input into the LSTM,which enhanced the sample diversity and reduced the data dimension.Center loss was introduced into the Softmax loss to make the intra-class distance smaller and further improve the classification accuracy.The bearing data set of Western Reserve University was used for experiment.It is verified that the proposed method has high accuracy and good stability in the identification of rolling bearing fault states.