Aiming at the problems of long start-up time, slow response speed and poor stability when the load changes in the hammer mill control system in the feed processing industry. A PID control method based on BP neural network algorithm is proposed. Firstly, establishing the reference model of the transfer function of the combined system of the frequency converter and the hammer mill drive motor and analyzing its stability. Then, based on the analysis of conventional PID and fuzzy PID control algorithms, the adaptive neural network algorithm PID is applied to the control process of the hammer mill drive system. By building a simulation model for the control motor of the hammer mill and it is simulated and analyzed by the Simulink graphical programming function. And based on LABVIEW software, a testing platform for the hammer mill measurement and control system was built for experimental testing and analysis.The results show that the designed BP neural network PID controller can achieve good adaptive tracking for the speed reference model given by the feed crushing system, with faster response to step signals, smaller overshoot, and stronger anti-interference ability. The designed adaptive controller can automatically adjust PID parameters according to changes in working conditions, resulting in an average reduction of 5.16% in electricity consumption per ton of material and an average increase of 2.08% in productivity, The control of the spindle speed of the hammer mill is more precise, with smaller errors, and has high control accuracy and strong robustness, meeting the adaptive control requirements of the feed hammer mill drive system.