Abstract: Due to the lack of accurate real-time passenger flow data, passenger traffic organization of railway passenger station has been mainly relying on experience to allocate the equipment, personnel and other required resources. Therefore, the passenger flow monitoring and early warning system for railway passenger station is developed. Through effective use of railway passenger ticket reservation data, historical station passenger flow data, real-time passenger flow data in and out of the station, train delay data and other relevant data, the monitoring, forecasting and over-limit warning of daily passenger flow, time-phased passenger flow of the station and passenger flow in waiting rooms can be realized using a passenger flow prediction support vector regression model based on K-mean clustering, which can facilitate the station staff to grasp the dynamic changes in passenger flow at any time so as to timely deploy the equipment and personnel, thus carrying out the passenger traffic organization operations of the station more accurately, efficiently, safely and orderly, and helping improve the level of passenger service at railway passenger stations and enhancing passenger travel experience.