Aiming at the problem that the traditional cognition techniques are not adaptive to the uncertainty and complexity in the Weapon SystemofSystems (WSoS) confrontation environment, a Command and Control (C2) algorithm based on Reinforcement Learning (RL) is proposed for the WSoS confrontation simulation at the tactical level. The UML architecture of WSoS that consists of a communication class, scouting class, attacking class, command class, supplying class and repairing class is designed and the battle simulation platform with the battle scenario is introduced. Then, based on the illustration and hypothesis for the command agent’s cognition problem, the parameter’s normalization, the discrete of the Q table based on GRBF neural network, the strip temporal difference mechanism and the learning process of the structure of the network are explained for the improved Qleaning cognition algorithm. Finally, the validation and effectiveness of the algorithm is proved through the battle simulation experiment of the airground unify confrontation SoS. Besides, through a lot of visualization recall analysis for the C2 algorithm, we found that the coordination of the firepower and the continuous tactical maneuver are important to the operational effectiveness and injure decrease.