With the development of intelligent weapons,the traditional training methods could notmeet the demands of large-scale modern warfare.In the past decade,artificial intelligence (AI) methods such as deep reinforcement learning have made great breakthroughs in chess and electronic competitive games.It proves that the AI methods have great advantages in solving large searching space problems.Furthermore,the problems of situation prediction and temporary adjustment could be solved more effectively by AI methods.A new method for modeling of air target threat is proposed based on the research of deep reinforcement learning.The parallel scene modeling technology and the air target behavior modeling technology are used to construct the model of deep reinforcement learning.The convergence penetration strategy is calculated with iterative learning under the scene of single airplane.The successful attempt verifies that the practicability of deep reinforcement learning in modeling of air target threat.It provides support for the further research on the modeling of fleet joint air defense.