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Mill load is an important index to evaluate the running state of the mill and predict the behavior of the mill. Aiming at the problem that the load is difficult to detect during the grinding process of the grinding mill and the load state cannot be accurately determined, an improved particle swarm optimization algorithm (IPSO) Optimized the radial load-based neural network (RBF) parameters of the mill load prediction model (IPSO-RBF), so that the inertia weight factor decreases nonlinearly in the iterative process, balancing the local search ability and the global The contradiction between search capabilities, the algorithm can quickly and accurately find the optimal solution, improve the prediction accuracy of the mill mill load. Through the experimental comparison of the measured data of the cement plant, the results show that the prediction accuracy based on the IPSO-RBF model is the highest, and the prediction results are compared with the real values. Root Mean Square Error (RMSE) and mean square error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination () were 0.210 2, 0.044 2, 0.161 7, 1.778%, and 0.978 2, respectively.