［Objective］ Address the current issues of poor internal flow stability,low single -machine efficiency,and subpar polishing quality in rice polishing units.［Methods］ Firstly,the traditional polishing machine was improved,its control parameters were clarified,and the mathematical model of the rice polishing unit was established.Then,the overall communication system of the unit was established,the working parameters of the unit were collected in real time,and the rice quality analyzer was introduced to detect the polishing quality indicators of the unit in real time.Built a database to store relevant data and participated in the optimization of the processing parameters of the unit.Finally,the parameters of the PID controller of the traditional unit were trained by the BP neural network,and the whale algorithm (WOA ) was introduced to optimize the BP neural network to achieve fast and accurate control of the rice polishing unit.［Results］］ Compared with the traditional PID control unit,the overall fragmentation rate of the BP -PID controlled unit optimized by the WAO algorithm was reduced by about 2%,the control time was reduced by about 30%,and the temperature rise decreased by about 2 ℃ at room temperature.［Conclusion］ The control system can effectively complete the adjustment of the processing parameters of the unit and has a good control effect.