Severe weather events constitute a significant safety hazard for urban rail transit systems, emphasizing the importance of risk management. This paper introduces a resilience evaluation method for urban rail transit lines during adverse weather conditions, leveraging Bayesian networks. The proposed method encompasses a comprehensive resilience indicator system tailored to severe weather scenarios, encompassing topological characteristics, passenger organization, and equipment factors. A risk analysis framework utilizing a risk matrix is then applied to evaluate subsystem vulnerabilities under various weather conditions. Furthermore, a Bayesian network-based resilience evaluation approach is designed to integrate and analyze these diverse factors. To demonstrate the effectiveness of this approach, a case study is conducted using real-world data from the Changping Line of Beijing Subway, resulting in resilience scores for critical components like elevators, 400V, traction power supply, and on-board equipment, as well as an overall resilience value for the entire line. The results indicate that, on rainy days, the resilience levels of Changping Line's key indicators and the entire line are precisely predicted by the proposed method. Furthermore, the key indicators and the overall line are generally anticipated to exhibit high resilience, which closely aligns with actual operational performance. These findings not only establish the accuracy of the resilience evaluation method but also reinforce its practical relevance and applicability in real-world settings.