Aiming at the problems such as neglecting the dynamic changes in the conventional missile attacking envelop solution, a missile attack envelop classification model based on deep belief network was proposed. The missile attack envelop was divided into five parts according to the relationship between missile hits and target maneuvers. By analyzing the situation parameters which affect the attack result of air-to-air missile, a missile attack prediction model was constructed. In the experiments, the reconstruction error and the test error rate were used to determine the network structure. Through extracting data layer by layer, the features of parameters were analyzed and the approaches of fine-tuning data sampling were discussed. Back propagation network and support vector machine were selected for classification comparison experiments. The results show that the deep belief network performs better than the other two algorithms in speed and prediction accuracy and the presented method is of great practical value.