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In the process of UAV driving, the queue information defined by the base station host will have a certain impact on the steering behavior related to the UAV. Therefore, in order to ensure the stable running state of the UAV, the distributed UAV queue control algorithm is studied based on reinforcement iterative learning. Calculate the specific numerical value of the reinforcement learning function, and through iterative processing, achieve the distribution representation of the probability coefficient of the iterative value, and complete the design of the reinforcement iterative learning model. Based on this, define the topology structure of the drone queue, and solve the specific numerical value of the information transfer index to achieve the drone queue information transfer based on reinforcement iterative learning. With the cooperation of the UAV queue controller, the distributed queue information set is established, and the queue data samples in it are combined to solve the UAV control parameters. Then, according to the modeling conditions of the travel queue, the control algorithm execution process is improved, and the design of the distributed UAV queue control method based on enhanced iterative learning is completed. The experimental results show that under the influence of the reinforcement iterative learning model, the steering angle of the unmanned aerial vehicle remains within the numerical range of 0 ° -90 °, indicating that the aircraft can always maintain a relatively stable motion state according to the queue information defined by the base station host, which meets the practical application requirements.