In order to facilitate large-scale data analysis and application within the railway industry, the central integration of data from various business departments across the network is imperative. This integration process encounters numerous challenges, as the complex multi-source data derived from diverse business units and systems presents significant issues related to data complexity and heterogeneity. These challenges underscore the urgent necessity for a well-structured and effective data fusion architecture, which plays a critical role in the handling and integration of such data. This paper presents a comprehensive study and design of a multi-source data fusion architecture tailored for the railway sector, addressing the specific challenges encountered during the data fusion process. The research emphasizes the key stages of the fusion process, including data processing, fusion levels, and fusion algorithms, thereby providing a structured framework for data integration. Moreover, the study outlines the functional requirements for platform tools and software that are essential for facilitating data integration, analysis, and sharing within the railway industry. By providing a robust architecture and detailing the necessary functionalities, this paper aims to enhance the efficiency and effectiveness of data utilization in railway operations, ultimately supporting informed decision-making and operational excellence.