Aiming at the problems of poor positioning and low grasping efficiency of industrial parts in the manipulator grasping assembly task of visual guidance positioning, an improved YOLO V3 intelligent grasping system solution is proposed to realize the intelligentization of industrial parts from object detection to automatic grasping. First, in order to improve the detection performance of small targets and crowded targets, an improved YOLO V3 target detection network is proposed. Secondly, data collection and training are carried out on industrial parts to realize the target recognition and positioning of the parts. Finally, through camera calibration and hand-eye calibration, the transformation from the image coordinate system to the world coordinate system is realized, the world coordinates of the grasped object are obtained, the grasping plan of the manipulator is carried out, and the grasping of the target object is completed. In the experiment, the Kinect V2 camera and the UR3 six-axis collaborative manipulator were used to form a grasping experiment platform, and the positioning and grasping experiments of the target parts were carried out respectively. The experimental results show that the improved YOLO V3 adds the fourth layer of feature-scale target detection, which improves the detection performance of small targets and crowded targets. The grasping system accurately locates the parts and successfully grasps them.