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Aiming at the characteristics of surface mount technology (SMT) solder joint image on the production line, a solder joint defect identification method based on PCA and particle swarm optimization algorithm-error backpropagation (PSO-BP) neural network is proposed. Firstly, image processing technology and CCD sensor are used to preprocess PCB solder joint image. Median filtering, gray image enhancement and global threshold method are used to effectively suppress noise interference and improve image contrast, and extract better image features. Then, the principal components analysis method is used to extract the five principal components including the 86.6% characteristic information of the solder joints, and input into the BP neural network improved by the particle swarm optimization algorithm. Through specific experimental analysis, the results show that the improved BP neural network has better recognition and classification effect, and can identify four different types of solder joints of normal, multi-tin, less tin and miss welding, with an accuracy rate of 93.22%. Reliable, it can effectively improve the detection efficiency in actual production.