Aiming at the problem that the area of printed circuit board is small and there are many electronic device solder joints on it, which is difficult to detect effectively by traditional detection methods, a surface solder joint detection algorithm of printed circuit board based on GhostNet-YOLOv4 is proposed. First, the backbone network of YOLOv4 is modified to enhance the feature extraction capability. Secondly, adding attention mechanism makes the network pay more attention to defect features. Finally, use GhostNet instead of CSPDarknet53 as the backbone network. Compared with the traditional PCB detection algorithm, this algorithm improves the detection accuracy and speed, and can realize the accurate detection and rapid classification of common defects such as broken circuit, missing welding and short circuit on the surface of PCB. Experiments on PCB data sets show that: the improved algorithm has good practicability, the accuracy on the test set is 86.68%, FPS reached 25.43, can meet the actual detection requirements of printed circuit boards.