Aiming at the autonomous maneuver decision-making problem of UAV air combat,a lateral maneuver decision-making algorithm is designed.By adding heuristic factors and double Q-table alternating learning mechanism,the shortcomings of traditional Q-Learning algorithm,such as slow learning speed and many ineffective learning,are overcome.Through path planning simulation and the comparison of data,it is verified that the improved Q-Learning algorithm has better stability and solving ability.A dynamic grid planning environment is designed,which can make the UAV adjust the grid size adaptively according to the changing of air combat situation,and has no impact on the solution rate.Based on the Q-Learning algorithm,the lateral maneuver decision-making model of UAV air combat is constructed,and it is verified that the improved Q-Learning algorithm can play a significant role in improving the winning / losing ratio of UAV air combat through the exchange of weapon platforms.