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In order to solve the problem of low efficiency of traditional methods for weld quality detection in industrial laser welding, a method based on convolution neural network for detecting weld defects on the surface of industrial steel plate is proposed. Firstly, based on convolutional neural network, a multi classification model framework is built, and the functions and related parameters used in each layer are analyzed; Then, the weld data are collected based on Industrial CNC machine tools and industrial cameras, and these data are preprocessed by classification, enhancement and amplification; Finally, based on the CNC machine axis, the sliding window detection method is used to collect the actual image to be tested, and the performance evaluation of the traditional machine learning algorithm in this kind of image data is compared through experiments. The experimental results show that the accuracy of the multi classification model trained by convolution neural network can reach more than 97%, and the test time of each image to be tested is about 300ms, which is far beyond the machine learning algorithm, and can meet the actual industrial requirements in accuracy and real-time.