The existing railway wagon flow forecast method cannot meet the demand of the actual railway transport organization. The old system is embodied in the slow computing speed and low accuracy. The railway wagon flow forecast system based on Hadoop-Hazelcast is designed to improve the accuracy of the forecast, shorten the calculation time and expand the scale of calculation. Using the Hadoop framework to analyze history wagon flow data to get the wagon flow data feature, using Hazelcast architecture to solve IO bottlenecks, the new forecast system integrates the scattered memory into memory computing cluster to compute time window in parallel. The results show that this method and system can greatly improve the forecast accuracy and speed.