To solve the problem of maneuvering decision in the autonomous air combat of unmanned combat aerial vehicle, the existing research achievements are analyzed and a maneuvering decision model that combines optimization idea with machine learning is proposed. The multi-objective optimization method is used as the core of decision model, which solves the problem of setting weight for multiple optimization targets and improves the extensibility of decision model. On the basis of multi-objective optimization, an evaluation network is trained by reinforcement learning and used for auxiliary decision-making to enhance the antagonism of decision model. In order to test the performance of decision model, with the background of short-range air combat, three simulation experiments are designed to test the feasibility of multi-objective optimization method, the effectiveness of auxiliary decision network and the overall performance of decision model. The simulation results show that the maneuvering decision model can be used in real-time confrontation with the maneuvering enemy aircraft.