In order to solve the problem of poor adaptability of the existing fuzzy PID control algorithm when the train is running at high speed in complex environment, the tracking error of the train will be large when it is disturbed by external factors. This paper proposes a train speed control algorithm based on RBF neural network PID control. Firstly, when building the optimization model of the train, the characteristics that the train must be in the inert condition when passing through the phase separation area are fully considered. According to the characteristics of the electric phase separation and speed limit conditions, the sections in the process of the train running are divided to simplify the solving process. Then, the RBF neural network PID controller is used to track and simulate the target speed curve. At the same time, the existing fuzzy PID control comparison was made. The results show that the speed tracking algorithm based on RBF neural network is effective.