Noted the limitation of existing methods to study the delay propagation mechanism in high-speed railway network, the Bayesian network was used to analyze delay dependencies between stations based on high-speed train operation data and schedule data. The percolation theory of complex network was applied to study the evolution of delay propagation clusters. Nanjingnan Railway Station in the largest delay propagation cluster and Changshanan Railway Station in the second largest delay propagation cluster were taken as examples to analyze the network at percolation threshold. Based on core delay propagation clusters, the station delay state prediction model was established. The results show that stations can be divided into 3 categories, namely, delay generator, delay mediator and delay absorber according to their characteristic in the aspect of delay propagation. Delay generator can not only spread the delay to stations close to them, but also spread the delay to the far away stations through delay propagation chain, which makes the network taking part of the delay generator as the center and spreading delay to the delay mediator and the delay absorber in a radiational delay propagation mode.