The invention discloses an unmanned aerial vehicle network hovering position optimization method based on multi-agent deep reinforcement learning. The method comprises the following steps: firstly, modeling a channel model, a coverage model and an energy loss model in an unmanned aerial vehicle to ground communication scene; modeling a throughput maximization problem of the unmanned aerial vehicleto ground communication network into a locally observable Markov decision process; obtaining local observation information and instantaneous rewards through continuous interaction between the unmanned aerial vehicle and the environment, conducting centralized training based on the information to obtain a distributed strategy network; deploying a strategy network in each unmanned aerial vehicle, so that each unmanned aerial vehicle can obtain a moving direction and a moving distance decision based on local observation information of the unmanned aerial vehicle, adjusts the hovering position, and carries out distributed cooperation. In addition, proportional fair scheduling and unmanned aerial vehicle energy consumption loss information are introduced into an instantaneous reward function,the fairness of the unmanned aerial vehicles for ground user services is guaranteed while the throughput is improved, energy consumption loss is reduced, and the unmanned aerial vehicle cluster can adapt to the dynamic environment.