In the intelligent frequency control strategy with large?scale wind power grid?connected system, only considering the CPS control criterion can easily cause the frequency off?limit in a short time, which seriously affects the control effect of the intelligent AGC control strategy. This paper proposes a multi?objective collaborative reward function reinforcement learning algorithm (TOPQ?MORL) intelligent frequency control strategy, which constructs a collaborative reward function that takes into account the multi?dimensional frequency control performance evaluation criteria, and realizes the coordinating evaluation of multi?dimensional frequency control performance standards on the time scale .The TOPQ learning strategy is used to optimize the action space of the agent globally, which effectively solves the problem of poor calculation efficiency of the Q function linear weighted multi?objective reinforcement learning algorithm under the traditional greedy strategy. The simulation results of the AGC control model of the standard two?region interconnected power grid shows that the intelligent AGC control strategy proposed in this paper can effectively improve the frequency control performance and improve the frequency quality of the system on the full?time scale obviously.