The current control methods in the grain drying process mainly focus on ensuring uniform moisture content, energy saving, and emission reduction. However, precise control of drying quality remains a bottleneck problem. To address issues such as strong coupling, nonlinearity, and difficult quality control in the grain drying process, this paper proposes the utilization of long and short-term memory neural network (LSTM) coupled with model predictive control (MPC) based on previous studies. This combined controller was applied to regulate the grain drying process according to a reference diagram for high-quality rice targeted regulation during the drying process. The simulation environment was used to compare the control accuracy of these two controllers. The effectiveness of system control was evaluated through continuous rice drying tests conducted both before and after the incorporation of the process reference diagram. Results demonstrate that compared to conventional PID controller, the LSTM-MPC controller exhibits stronger robustness and faster response speed. Furthermore, by incorporating the process reference diagram into control decisions, visualization of the drying process quality index can be achieved while significantly improving overall drying quality levels.