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Aiming at the problems of low signal-to-noise ratio of tool fault diagnosis signals and inaccurate diagnosis results, local mean value decomposition (LMD) combined with permutation entropy (PE) is used to process the collected vibration signals during tool processing, and then the extracted feature vectors Input to the trained long and short-term memory neural network (LSTM) to get the diagnosis result. In order to improve the diagnosis efficiency of LSTM, combined with convolutional neural network (CNN) to transform LSTM. Experiments show that the diagnostic accuracy of the method proposed in this paper is nearly 12% higher than that of the BP neural network, and the improved LSTM network reduces the diagnostic time of the traditional LSTM by 50%.