Abstract: Complex electronic equipment has complex structural composition, multiple assembly processes, high precision requirements, high degree of coupling between electromechanical and hydraulic systems, and difficulties in online detection. If there are any assembly errors in the components, it will lead to rework. In order to achieve online detection of the quality of the assembly process, an image detection system based on machine learning is designed. Firstly, a movable image acquisition device and camera layout scheme is designed based on the size of the equipment, which can be applied to image acquisition requirements of different sizes of equipment. Secondly, the distortion problems existing in the acquired images are corrected, and the segmented images are stitched through image fusion. Finally, deep learning algorithms are used to achieve image recognition, and knowledge graph is used to restore detection results. The results show that the system can detect defects in the assembly process in a timely manner, with an accuracy rate of over 99. 5% for identifying missing and incorrect assemblies, meeting the needs of detecting the assembly status of complex electronic equipment parts.