In order to improve the autonomous ability of un-manned aerial vehicles (UAV) to implement air combat mission, many artificial intelligence-based autonomous air combat mane-uver decision-making studies have been carried out, but these studies are often aimed at individual decision-making in 1v1 scenarios which rarely happen in actual air combat. Based on the research of the 1v1 autonomous air combat maneuver de-cision, this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning. Firstly, a bidirectional recurrent neural network (BRNN) is used to achieve communication between UAV individuals, and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established. Secondly, through combining with target allocation and air combat situ-ation assessment, the tactical goal of the formation is merged with the reinforcement learning goal of every UAV, and a coope-rative tactical maneuver policy is generated. The simulation re-sults prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooper-ative maneuver policy through reinforcement learning, the co-operative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.