Abstract: The tailings thickening process is a complex system with multiple inputs, multiple outputs, and high time delay. Exploring the multi−objective optimization problem of tailings thickening under different factors is of great significance, which provides reference for promoting precise control and intelligent development of tailings thickening process. A semi−industrial intelligent testing device for dynamic thickening of tailings was developed to conduct orthogonal experiments on dynamic thickening of tailings, and investigate the effects of mud layer height, feed flow rate, and rake speed on the multi−objective thickening of tailings; A multiple regression model was established for underflow concentration, overflow turbidity, and rake torque based on the results of semi−industrial orthogonal experiments for dynamic thickening of tailings. Using the communication module of MATLAB software, the real−time prediction of the semi−industrial test effect of dynamic thickening of tailings was achieved; Taking into account the actual demand of mines for tailings thickening, a multi−objective optimization model for dynamic tailings thickening based on NSGA−II algorithm was constructed. The optimized parameters and thickening effects of dynamic tailings thickening were obtained. The research results indicate that the height of the mud layer and the feed flow rate had a significant impact on the thickening effect of tailings. The height of the mud layer was the most important factor affecting the thickening effect. The prediction difference of the multiple regression model for tailings thickening process was within 4.53%, and the model fitting effect was good; The optimized parameters for tailings thickening after multi−objective optimization were mud layer height of 0.30 m, feed flow rate of 0.91 m³/h, and rake speed of 3.80 r/min. The optimized tailings thickening effect parameters were underflow mass concentration of 69.57%, overflow turbidity of 40.41 NTU, and rake torque of 11.53 N·m.