Abstract: Intelligent diagnosis of bearings is the key to the intelligent diagnosis of rotating equipment. Actual bearing fault diagnosis is exposed to problems such as incomplete feature extraction and low efficiency of traditional diagnosis methods under variable working conditions. To solve these problems, a combination method is hereby proposed, and deep-level features of the original vibration signal are extracted using a deep feature extraction network combining a convolutional neural network with a wide convolution kernel and a long-term and short-term memory network. Besides, the knowledge transfer between the source domain and the target domain is realized by a domain-adversarial training of the neural network, which solves the problem of the unfavorable cross-domain diagnosis ability under variable conditions. The hereby proposed method is correspondingly verified, and the experimental results show that the proposed method can effectively extract the bearing vibration signals and identify the bearing fault types under variable working conditions, and improve the cross-domain diagnosis ability.