An adaptive proximal policy optimization algorithm based on mission-completion degree was proposed to address the challenge of maintaining large-separation formations for high-speed unmanned aerial vehicles (UAVs) in unknown and dynamic environments during manned-unmanned cooperative operations. An observation space that depended solely on relative state information was constructed, and a clipping-coefficient tuning mechanism driven by mission completion degree was devised. A segmented trajectory-truncation method was incorporated to accelerate convergence and enhance control stability. The simulation results indicate that relative to the baseline proximal policy optimization algorithm, marked improvements in convergence efficiency and tracking accuracy are achieved. Accurate formation is preserved even when the manned aircraft performs random maneuvers or abrupt heading changes, so the effectiveness and robustness of the approach in dynamic combat scenarios are validated, and an efficient solution for manned-unmanned formation keeping is provided.