With the wide application of highend equipment in the industrial field, its operating state has a great impact on the safety of equipment and the performance of products, sudden failures often cause huge loss of people's lives and property and affect the safety and stability of society. The electromechanical system is in the state of variable speed operation, and its state characteristic information is difficult to obtain, which makes it difficult to diagnose and predict the fault of the electromechanical system. In view of this problem, a fault classification, recognition and diagnosis model of electromechanical system based on deep learning is proposed. Firstly, the vibration signals of the key parts are converted into timefrequency graphs by timefrequency transformation to form the input samples；Secondly, the samples were input into the deep learning neural network for feature learning and state recognition, the method of combining different transformations and deep learning convolutional neural networks is studied, which is applied to a mechanical and electrical system test bench to compare the fault state classification performance. The experimental results show that this method provides a new way for the fault diagnosis of electromechanical system.