［Objective］In response to four major challenges faced in the development of urban rail transit O&M (operation and maintenance), Nanning Rail Transit Line 4 is taken as the practice platform to construct an intelligent O&M system for signaling system driven by data and intelligent decision-making. The goal is to break through the technical and managerial bottlenecks of traditional O&M models and achieve efficient, precise, and sustainable O&M system.［Method］By integrating multi-source heterogeneous data and building a multi-layer architecture, the system enables real-time equipment status monitoring and degradation trend analysis, significantly improving data utilization and fault identification capabilities. By leveraging complex network theory and equipment failure mechanism analysis, a fault prediction model covering key equipment is developed to support automatic fault localization and early warning. In terms of management, through refactoring digital workflows, allocating light-asset material, and establishing cross-disciplinary integrated teams, the O&M system eliminates traditional data silos and professional barriers, forming an ecosystem encompassing ′personnel-equipment-material method synergy′.［Result & Conclusion］In the engineering application of Nanning Rail Transit Line 4, this intelligent system promotes three major transformations in the O&M model: from passive response to proactive prevention, from experience-driven to data-driven, and from fragmented management to coordinated optimization. Practice has demonstrated that the intelligent O&M system, empowered by intelligent technologies and process re-engineering, significantly enhances O&M efficiency and equipment reliability, offering a replicable solution for the industry. Research in the future will further explore the application of cutting-edge technologies such as artificial intelligence and machine self-learning in intelligent O&M systems, promoting the evolution of rail transit O&M system towards greater intelligence and sustainability.