In order to solve the problem of urban traffic congestion, the state advocates travelling by public transportation, and the use of bus smart cards has become more common. At present, for the data generated by intercity bus smart card travel, there is very little research on the time between bus stations. Therefore, the power-law distribution analysis of running time between bus stations based on machine learning technology is proposed. The station algorithm is used to divide the bus stations in the city, and the running time of the bus in the two adjacent stations is obtained. The time interval data were fitted linearly. Using two data sets from a city in South China and a city in North China, the results show that the bus running time interval is in accordance with the power exponential distribution; the time interval of bus operation is in line with human behavior dynamics.