Combining the advantages of generalized regression neural network (GRNN) in nonlinear fitting and flexible network structure, a prediction model for the anticompression strength of cooked pellets is constructed to determine the proportion of raw materials (Ca, Si, Mg, etc.) and the important parameters characterizing the quality of pellets in the pellet production process. Based on the anticompression strength prediction model and with beetle antennae search (BAS) algorithm, an intelligent recommendation model for optimum pellet ingredient proportion is constructed. In the adjustable range of pellet ingredient, the intelligent recommendation scheme for optimum pellet ingredient proportion is presented. The simulation and experiment results of the recommended scheme show that the prediction model of cooked ball anticompression strength has super interpolation ability and excellent generalization performance. In the pellet ingredient change range of no more than 20%, the intelligent recommended optimal pellet ingredient proportion scheme can increase the cooked ball anticompression strength by more than 16% on average; the system operates steadily and the simulation results are effective. The BAS intelligent recommendation model was applied in the actual pellet manufacturing process, compared with that in the same period of the previous year, the daily average anticompression strength of cooked pellets is improved significantly and the practical application effect is good.