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5G communication networks have the characteristics of high concurrency and low latency, and a large number of devices are involved in the network, including base stations, antennas, Repeaters, etc. In high-density scenarios, a large number of user devices are connected to the base station at the same time, resulting in a large increase in network capacity requirements. As a result, some links have insufficient resources while others have excess resources, resulting in load imbalance and link congestion. Therefore, a load balancing test system of high-density 5G communication link based on BP neural network is designed. Set up the communication link load balancing test framework, design the controller, traffic monitor, network processor, and complete the system hardware design. Build a communication link channel transmission model, calculate the link load, apply traffic monitor and network processor to obtain the historical load value of the communication link, use BP neural network to predict the load value of the communication link at the next moment, combine the fitness function with the actual load capacity of the link, and terminate the neural network training when the load imbalance reaches the minimum. Obtain the best solution of communication link load balancing test, so as to achieve high-density 5G communication link load balancing. The experimental data show that under different experimental conditions, the maximum Jain"s fairness equilibrium index is 0.95, the minimum blocking rate of communication link is 5%, and the network throughput is between 12Gbps-18Gbps.