In high-speed railway signaling systems, the network structure of the Train Control Center and Inter-locking Integrated System (TIS) is highly complex, with a large number of interfaces, numerous redundant channels, and forwarding components such as switches. These factors result in challenges such as insufficient accuracy, low efficiency, and poor real-time performance in terms of network monitoring and fault diagnosis. As the scale of railway yards continues to expand, these issues are becoming increasingly prominent. To address these challenges, this paper proposes a network fault propagation model based on the Fuzzy Broad Learning System (FBLS). By leveraging nonlinear transformations and feature mapping techniques, FBLS can efficiently extract and analyze network fault features, even with a relatively small amount of data. Experimental results show that the FBLS-based diagnostic model achieves higher accuracy and faster response speed in fault identification and propagation path analysis compared to traditional graph theory and fuzzy reasoning methods. Further comparisons with existing methods validate the advantages of FBLS in handling multi-source data, improving noise tolerance, and adapting to large-scale railway yard network systems, demonstrating its broad application prospects in railway signaling systems.