To solve the problem of downlink power control of dense UAV networks, reduce the mutual interference of UAVs and improve the energy efficiency of the system, a downlink transmit power control algorithm for dense UAV networks is proposed. First, convert the power control problem of the UAV network into a mean field game theory model in a high-dimensional system state to reduce the mutual interference between UAVs.Second, convert the mean field game theory model into a Markov decision process, in order to obtain the equilibrium solution under the dense deployment of UAVs.In addition, a mean field game theory algorithm based on deep reinforcement learning is proposed, which obtains the optimal power control strategy of the system by using a deep neural network to maximize the energy efficiency of the system.Finally, the proposed method is compared with the other three algorithms through simulation analysis. The experimental results show that the proposed method can effectively interact between drones and the environment, effectively reduce the mutual interference of drones, and enhance the system network communication performance; at the same time, compared with the other three methods, the proposed method has a faster convergence Faster, more energy efficient, with good convergence and reliability.