To improve the coverage effect of UAV swarm deployment in random environments such as disasters and battlefields， this paper proposed a multi-UAV communication coverage deployment strategy based on adaptive virtual force particle swarm optimization. Firstly， the gravity model was introduced into the traditional particle swarm optimization algorithm to modify the fitness function of the particle swarm optimization algorithm to achieve the balance between coverage and energy consumption. Secondly， in order to reduce the communication interference between UAV， an adaptive lift control strategy based on the exclusion model is designed to reduce the repeated coverage between multiple UAV. Finally， five algorithms were selected for comparative analysis to verify the effectiveness of the proposed method. The experimental results show that， while ensuring the maximum area coverage， the repeated coverage rate of the proposed method is only 21.01%， and the average energy consumption is only 4.02×10² kJ， which is significantly lower than other comparison algorithms. In addition， the proposed method can effectively reduce communication interference and resource waste， avoid falling into local optimum， and is not limited by the number of UAV. It has the ability to deploy in an environment with random changes in the number of UAV， showing good environmental adaptability.