The invention discloses a behavior simulation training method for an air intelligent game. The method comprises the following steps: S1, constructing an intelligent agent game decision model; S2, determining an environment state and an action space, and shaping a continuous non-sparse reward function of each action; S3, carrying out an air game in the model, and executing the following steps: S31, generating a next environment state according to an executed action, obtaining a reward, and carrying out loop iteration in sequence to realize maximum accumulated reward; S32, realizing reverse reinforcement learning based on expert behaviors, and obtaining a target reward function; S33, calculating the similarity between each agent behavior and the expert behavior; S34, obtaining a comprehensive reward; and S4, training the agent game decision model. According to the method, a traditional low-efficiency reward function design process and a model training random exploration process are improved, so that the reward function has interpretability and human intervention ability, the agent decision level and convergence speed are improved, and the cold start problem of model training is solved.