To solve the problem of autonomous evasion of the carrier aircraft facing incoming enemy missiles, a deep reinforcement learning method based on the improved DDPG algorithm is adopted for training and learning.In addition to considering the evasion performance in the reward function, rewarding models are established respectively for the cost of the aircrafts altitude maintenance and speed maintenance, as well as the relative altitude change and the approaching speed change of the incoming missile.Finally, training simulation tests and analysis were conducted based on the aircraft model.Through simulation, it can be seen that the training results can effectively realize the evasion decision of the incoming missile, and the designed reward function and input parameters can also play a correct role, and the results have certain generalization ability.