Unmanned combat systems, as a new type of combat force composed of unmanned platforms with different combat capabilities, have significant advantages of high flexibility and low cost. However, the strong confrontational nature of modern warfare makes it vulnerable to attacks and paralysis. The existing recovery methods have problems such as poor effectiveness and slow speed, and it is urgent to enhance the adaptive recovery ability of this system in high-intensity confrontational environments. To address this issue, an optimization strategy is proposed to support the capability recovery of unmanned combat networks, aiming to enhance their rapid recovery ability and continuous combat effectiveness after being attacked. Firstly, an unmanned combat network model is constructed based on the OODA ring theory; Secondly, in combination with the concept of closed combat chains, the evaluation indicators of network combat capabilities are constructed from three dimensions: effectiveness, rapidity and redundancy. Then, a network capability recovery optimization model is established with the maximization of network combat capability as the objective function and the degree of network structure change and node load as the constraint conditions; Furthermore, an improved genetic algorithm is proposed for solution. The performance of the algorithm is enhanced by introducing a penalty function mechanism and adaptive crossover and mutation strategies. Simulation results show that the proposed strategy effectively reduces network structural variation and significantly enhances recovery capability, providing theoretical support for autonomous recovery of unmanned combat systems.