This paper studies a fuzzy neural network algorithm which can effectively promote the efficiency of urban rail transit operation organization and management. The overrun learning machine module is used to convolute the passenger flow of each station, the convolution neural network is used to summarize the passenger flow data of each station in the inspection line, and the reference data of other lines is constructed, and the binary module is used to form the release signal light recommendation data. After the application of the system, the peak vehicle load rate of passenger flow is significantly reduced, the estimated vehicle load rate of passenger flow is significantly increased, and the departure gap of passenger flow is significantly increased, but it does not affect the residence time of passengers in the station. It is considered that the algorithm can effectively improve the operation efficiency and economic benefits of urban rail transit.