In order to solve the problems of many kinds of coating defects of the lithium battery pole piece, the low accuracy of the traditional classification and detection methods, and strong artificial dependence, an automatic classification algorithm for coating defects of lithium battery pole piece based on the convolutional neural network was proposed. First, the network structure and model parameters are optimized, then the jump connection structure is added to the network, the multi-scale features extracted by dilated convolution are fused with the high-level features to obtain more defect features, and the LeakyReLU (Leaky Rectified Linear Unit, LeakyReLU) activation function is adopted to retain the negative feature information in the image. Finally, through the constructed data set training model, the accurate classification of the coating defects of the lithium battery pole piece is realized. Experimental results show that the recognition accuracy of the current method can reach 99.34%, and the average detection time is 51 ms. The improved method can accurately classify 18 kinds of coating defects of lithium battery pole pieces, which can meet the requirements of real-time classification and detection in industrial production.