Traditional sorting operation can not be adjusted with the change of working environment. In view of this shortcoming, a sorting robot is researched based on machine vision. By introducing image processing and feature engineering technology into the visual module, the sorting system can be adjusted in time. Unlike these methods, this research is based on the industrial sorting system in the laboratory and applies the deep learning method to it. By introducing faster RCNN detection algorithm into visual module and improving of Region Proposal Network (RPN), the detection process of faster RCNN model is accelerated, so that the system meets the real-time requirements of industry. faster RCNN, as an end-to-end method, can automatically generate more expressive features for input images and extract corresponding features for corresponding targets. This avoids the manual design features. Its automatic feature generation ability makes it suitable for various scenarios, which improves the environmental adaptability of industrial sorting robots.