Abstract: Subway stations serve as vital hubs in underground transportation systems and play a critical role in facilitating social and economic exchanges as well as interpersonal interactions. However, these stations are also susceptible to various disaster risks. Historical incidents, including earthquakes, have had severe consequences on underground transit networks, leading to significant disruptions in urban functionality. Therefore, assessing the resilience of key transportation hubs such as subway stations is crucial for enhancing urban safety and ensuring their continued functionality.The objective of this study is to provide a comprehensive resilience assessment framework for shallow-buried subway stations, taking into account the uncertainty of seismic intensity, with a focus on evaluating their robustness and rapid recovery capability in the event of an earthquake. The proposed framework involves the utilization of finite element software to build numerical models of relevant subway stations. Subsequently, a large number of numerical analyses are conducted using selected seismic motions to derive vulnerability functions for subway stations based on peak ground acceleration (PGA). Additionally, Monte Carlo simulations are employed to further quantify the uncertainty of seismic motion intensity, ultimately determining the probabilities of subway station damage at various stages.By integrating the probabilities of subway station damage with the relationship between damage and economic loss, as well as considering the recovery paths and recoverability of the stations, a comprehensive resilience assessment is achieved. This study evaluates and discusses the impacts of performance recovery models, site conditions, and disaster preparedness time on the seismic resilience of subway stations using the derived resilience index R. This research contributes to the design and management of subway networks based on resilience, enabling them to adapt to seismic disasters and facilitating the effective allocation of resources by relevant decision-makers.