Owing to the low accuracy and poor real-time ability of modern air combat maneuver decision-making, the BAS-TIMS (beetle antennae search-tactical immune maneuver system) algorithm was improved and applied to air combat maneuver decision-making. The traditional maneuver inventory was enlarged by adding four maneuvers, left climbing, right climbing, left diving, right diving. Eleven basic maneuver tactics were designed, and a method was given to control them. Considering range, height, speed, angle and the superiority function of fighter performance, a comprehensive superiority function of fighter performance was built with the help of nonparametric model. Monte Carlo probability iteration was introduced into the beetle antennae search algorithm to improve its global search ability and convergence speed. This algorithm was combined with TIMS, and the improved BAS-TIMS algorithm was applied into modern air combat maneuver decision-making. An example was given to simulate and prove the effectiveness by comparing BAS-TIMS with game theory, the improved symbiotic immune evolutionary algorithm, the traditional BAS algorithm and the traditional TIMS model. The simulation results show that the improved BAS-TIMS algorithm has more advantages on convergence speed, accuracy and global search ability in air combat maneuver decision-making.