In order to solve the current assembly robot vision system’s problems of high false detection rate, low efficiency, and difficulty in obtaining effective positioning information. a component vision recognition and positioning method based on deep learning was proposed. Firstly, a high-precision detection algorithm based on deep aggregation and decoupling head was designed to improve the accuracy of component identification and subject detection. Secondly, the rules of labeling and determination were designed, and the position subject outlines and grasping points were refined. Lastly, a lightweight detection algorithm based on network pruning was designed to accomplish model compression and improve the efficiency of pin detection and assembly point positioning. The research results show that the method has achieved better performance in the identification and positioning of components. The average error rate of category recognition is merely 0.27%. The calculation is reduced by 29.8%, and the volume of parameters decreased by 22.7%. Through this method, traditional component contour detection is extended to grasp point and assembly point positioning to obtain abundant category and position guideline information, laying a foundation for industrial robots to grasp and assemble accurately, reliably and stably.