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In order to meet the requirement of rapid and intelligent manufacturing of multi-batch and small-batch sheet metal parts in the fields of national defense, military industry and electronic information, a method of surface defect detection for sheet metal parts with few samples based on convolution neural network is proposed. Firstly, based on the network model of convolution neural network, the classical classification model is built, and the parameters are modified in the experiment to meet the needs of surface defect detection in actual production. Secondly, the sample set of convolution neural network training model is obtained by defect segmentation and extraction method, and the data are enhanced. The experimental results show that the accuracy of the model can reach 96.88%. Finally, the window sliding detection method is used to compare the part to be tested with the model to realize the classification of defects and the marking of defect location. Experiments show that the accuracy and real-time performance of the method meet the requirements of actual industrial production.