Artificial Intelligence Empowered Management of High-Speed Railway Complex Systems: Research Progress and Future Perspectives

High-speed railways, as networked complex systems characterized by strong coupling among multiple technological subsystems and dynamic evolution across the entire lifecycle, exhibit pronounced nonlinearity, multi-scale interactions, and multi-agent coordination during operation, posing substantial challenges to management. In recent years, the rapid development of artificial intelligence (AI) has provided crucial technical support for the intelligent transformation of high-speed railway management, and how to integrate AI into diverse business scenarios has become a key research focus. Building on complex systems theory, this article provides a systematic review of core AI methodologies and their application progress in the full-lifecycle management of high-speed railways, summarizes critical challenges including data governance, model–mechanism integration, interface fragmentation, and knowledge embedding, and, guided by four overarching objectives—safety, efficiency, economy, and service—proposes future directions for lifecycle-wide data fusion and cross-system intelligent collaboration. The findings aim to offer new insights into AI integration under the Intelligent High-Speed Railway 2.0 framework and to support the construction of a safer, more efficient, and sustainable intelligent railway management system.