Objective: The metro system, as a crucial component of modern urban transportation, relies heavily on the reliability of its traction power network to maintain stable operations. However, existing research on metro system resilience assessment often overlooks the complex coupling characteristics between the traction power network and the metro network. In particular, the many-to-one and one-to-many coupling characteristics of the traction power network significantly influence metro system resilience but remain underexplored. This study proposes a resilience assessment method for metro networks based on the network coupling characteristics, focusing on quantitatively evaluating the dynamic impact of traction power network failures on metro network operational performance under both partial and complete failure scenarios. Methods: This research constructs separate models for the traction power network and the metro network. Building on these foundational models, it incorporates the many-to-one and one-to-many power supply characteristics of the traction power network, establishing a coupling model that integrates both systems. Network efficiency, which considers passenger flow weighting and travel time impedance, forms the basis for assessing resilience. The Monte Carlo method is used to model the recovery process of the metro traction power network. Using the Xi'an metro network as a case study, different failure scenarios are simulated, enabling a comprehensive evaluation of the metro system's service capacity and resilience changes under various fault conditions. Results: The results of this study are as follows: (1) The many-to-one redundancy characteristic of the traction power network enhances metro network resilience by 6.8%-14.4%. However, ignoring the one-to-many characteristics of the traction power network may lead to an overestimation of resilience, as cascading failure effects are inadequately accounted for. (2) Traction power network failures in high passenger flow areas can cause efficiency losses of up to 50.2%, with corresponding resilience losses reaching 36.1%. (3) Resilience performance varies across metro stations and the overall network depending on the complexity of failure scenarios. More complex scenarios involve a greater number and broader distribution of repair targets, increasing the intricacy and time demand of recovery processes. Conclusions: The proposed metro network resilience assessment method based on network coupling characteristics provides a more accurate evaluation of the impact of traction power network failures. By accounting for both many-to-one and one-to-many coupling characteristics, the method realistically reflects the redundancy supply effect of the system and the cascading failure process. The study emphasizes that while adopting a decentralized layout, metro system operation and planning need to strengthen the redundancy design of traction substations and supply section networks. Furthermore, a coordinated emergency response across multiple departments is recommended to ensure rapid mobilization of repair resources and shuttle capacity, minimizing disruptions to passenger travel during emergencies. The findings of this study provide theoretical guidance for developing emergency response and recovery strategies in metro systems under power facility failure scenarios. Future research will expand the resilience assessment framework to multi-modal transportation systems, further improving the universality and practicality of the model.