Due to the development of stealth technology and the requirement of attack tactics, the carrier aircraft may enter the enemy missiles inescapable attack area.To solve the problem of maneuvering evasion of the aircraft in the inescapable attack area of radar-type medium/long-range air-to-air missiles, a model of evasion decision-making based on deep-learning network is designed.A maneuverable flight database is established by using Monte Carlo simulation method, and the network model is learned.The effectiveness of network output is verified by simulation.The simulation results show that, the network output after learning can satisfy the condition of maneuverable flight and has better decision-making effect in the pursuit-evasion simulation, which can significantly improve the probability of successful escape in the missiles inescapable attack area and provide a reference for the pilots maneuvering decision-making when they perform high-risk missions.