The networked operation of urban rail transit system is an inevitable trend for future development. However, risk identification in such systems will become increasingly complex. Therefore, exploring the topological structure of urban rail transit system is essential for improving their risk identification capability. This paper proposes a topology-based risk identification method for urban rail transit system. Based on historical data and system component structure, the method identifies risks from three aspects: personnel, physical structure, and external environment. A global risk network model is constructed using a physical structure-oriented urban rail transit system topology network, and a comprehensive risk measurement calculation method based on entropy weight-TOPSIS is established using composite complex network theory. Case studies demonstrate that the proposed method can identify key risk nodes in urban rail transit system and develop corresponding risk prevention and control strategies before accidents occur, thus reducing unnecessary maintenance costs.