Abstract: Currently, China's rail transit industry is at a critical juncture of transitioning from digitization to digital intelligence. The explosive deployment of large models and intelligent agent technologies has disrupted the operational rules of the traditional rail transit industry, which were based on linear division of labor, and is driving its comprehensive evolution toward a digital intelligence ecosystem characterized by data as the core production factor and multi-stakeholder collaborative innovation. Building on this, the existing research framework of rail transit industry intelligent transformation is systematically reviewed based on the latest research paradigms of industrial ecosystem theory. Firstly, the key components of the rail transit industry ecosystem and persistent systemic pain points such as data barriers and insufficient knowledge accumulation are clarified. The core value of large models and intelligent agents as new-generation technological engines is explained from two dimensions: capability deconstruction and empowerment mechanisms. Secondly, the reconstruction logic of industrial data elements, business processes, and stakeholder relationships is analyzed. Finally, the core trends in future ecosystem evolution and practical challenges such as reliability and standardization gaps during implementation are discussed. A phased implementation approach with ecosystem co-governance is proposed, aiming to provide theoretical references for systematic research and large-scale deployment of large models and intelligent agents in the rail transit field.