In view of the complex working environment of rolling bearings, the difficulty of extracting fault features from bearing vibration signals due to noise interference, and the low accuracy of traditional fault diagnosis algorithms, a rolling bearing fault diagnosis method based on complete ensemble empirical mode decomposition with adaptive noise analysis algorithm (CEEMDAN) combined with convolution neural network (CNN) and embedded long short-term memory neural network (LSTM) is proposed. Firstly, the original vibration signal of the bearing is decomposed by CEEMDAN algorithm to obtain the intrinsic mode function (IMF); Then the permutation entropy of the reconstructed signal is calculated and normalized as the eigenvector; Finally, the eigenvector is input into the deep learning model established by CNN-LSTM for diagnosis and recognition. The results show that the proposed method has faster fitting speed and higher accuracy, and the average fault diagnosis accuracy rate reaches 98.63%.