Pellet performance plays a crucial role in the subsequent smelting process in the furnace, so an im-proved multilayer perceptron (MLP) pellet metallurgical performance prediction improvement model is proposed. The improved model mainly consists of a multilayer perceptron and a gradient boost-ing framework (XGBoost), which ensures that the integrated model has the advantages of the XGBoost iterative training model that effectively improves the prediction accuracy, and also ensures the flexibility and extensiveness of the MLP model in dealing with nonlinear problems and largescale data sets. The model is trained with a large amount of raw pellet raw material ratio and pellet ore metallurgical performance data to quantitatively characterise the relationship between pellet raw mate-rial ratio information and pellet ore metallurgical performance. Combined with the simulation results, the prediction results of RDI, ΔT, RI and RSI are better than some existing prediction algorithms, and the prediction of the metallurgical properties of pellet ores is achieved.