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In order to solve the problem of excessive interruption probability of data scheduling behavior in unmanned aerial vehicle communication networks and achieve reasonable allocation of communication resources, research was conducted on data scheduling control methods for unmanned aerial vehicle relay communication based on improved machine learning. Design a basic network architecture, combine the BMRC protocol, set up URLLC data link units, combine relevant communication data samples, and solve specific values of communication interruption probability to achieve joint optimization processing of UAV relay communication network resources. Calculate the slot allocation parameters and bandwidth allocation parameters respectively, and based on this, determine the relay location of the UAV to achieve scheduling of relay communication resources. According to the machine learning algorithm standards, define PCA improvement features to improve the machine learning algorithm, and then combine the optimal controller closed-loop to achieve control of communication data scheduling behavior. Complete the design of a drone relay communication data scheduling control method based on improved machine learning. The experimental results show that under the influence of improved machine learning algorithms, as the accumulation of relay data increases, the maximum probability of interruption in data scheduling behavior in unmanned aerial vehicle communication networks can only reach 7.3%, effectively reducing the probability of interruption and meeting the practical application requirements of reasonable allocation of communication resources.