To address the issues of poor visual feedback tracking accuracy and long time consumption in sorting robots,a visual feedback tracking method based on PSO-BP neural network for sorting robots is studied to improve the visual feedback tracking effect.Based on the visual feedback information of the sorting robot,the kinematics model of the sorting robot was established,and the error function of the output position and input position of the sorting robot arm was solved.PSO is used to optimize the weight and bias of BP neural network.In the BP neural network optimized by weight and bias,the error function is input to predict the visual feedback tracking control quantity of sorting robot.The predictive visual feedback tracking control quantity is used to adjust the parameters of incremental PID online,and the high-precision visual feedback tracking control quantity of sorting robot is output to realize the visual feedback tracking of sorting robot.Experimental results show that this method can effectively visually track and feedback the joint angles of the sorting robot's mechanical arm.In the presence of interference,the step response stabilizes around 10 seconds of operation time.When there is interference,the average error of visual feedback tracking using this method is 0.09 cm, with an average time consumption of 0.10 ms.Without interference,the average error is 0.03 cm,and the average time consumption is 0.04 ms.