The identification and classification technology of printed circuit board (Printed Circuit Board) surface mount components plays an important role in the production process of modern electronics industry. A target detection method based on YOLO v3. First, an industrial camera is used with an optical lens to construct a data set of mounted components, and secondly, the feature pyramid structure FPN (Feature Pyramid Networks) of YOLO v3 is redesigned, and then the K-means method is used to improve the clustering of the mounted component data set. Get the Mouted anchor and corresponding parameters. Finally, use Mounted anchor and network structure to retrain the improved YOLO v3, and compare experiments with the original network to verify the recognition and classification effect of the mounted components. The experimental results show that the improved YOLO v3 mounted component recognition and classification technology has an average accuracy rate of 9% higher than that of the original network, and a slight increase in the recall rate.