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Bearing is one of the important parts of mechanical equipment, and it is also one of the main fault parts of mechanical equipment. Bearing failure is the focus of mechanical equipment, and faulty bearings directly affect the use of mechanical equipment. Aiming at the problem of low diagnosis accuracy of bearing fault diagnosis based on traditional convolutional neural network algorithms, this paper proposes an optimization method based on signal feature extraction and convolutional neural network. Firstly, the signal characteristics in the time domain and frequency domain are extracted from the original data signal to obtain the effective fault characteristic values. Then, the convolutional neural network is used to diagnose the extracted feature values and complete the fault classification. In this paper, the rolling bearing vibration acceleration signal of Case Western Reserve University is used as a data set to verify the proposed method, and the average accuracy of the fault diagnosis is 74.37%, the variance of the accuracy is 0.0001, and the average fault diagnosis accuracy of the unoptimized algorithm is 65.6%. The variance of the accuracy is 0.0019. Experimental results show that compared with the traditional convolutional neural network, the proposed method has a significant improvement in the accuracy of bearing fault diagnosis, and the method has better stability, less calculation time and better comprehensive performance.