## 1 Introduction
Based on the identified research gaps, this study seeks to quantify resilience characteristics through rigorous analytical methods, assess network performance under heterogeneous disturbance conditions, and evaluate the relative effectiveness of alternative recovery strategies in restoring network functionality. Predicated upon these research objectives and the synthesis of the extant literature, this investigation addresses critical knowledge gaps in railway network resilience studies by formulating three specific hypotheses:  
1. Different types of disturbance events (external attacks, natural disasters, technical failures, and passenger/freight flow fluctuations) will impact railway network resilience in distinct patterns;
2. The resilience of railway networks can be comprehensively evaluated through the integration of three complementary measures: accessibility, invulnerability, and restorability;
3. Strategic recovery approaches based on topological importance (betweenness centrality and node degree) will significantly outperform random recovery strategies in restoring network functionality. The primary aim of this study is to develop and validate a comprehensive resilience evaluation framework that bridges physical and service network perspectives, thereby providing railway administrators with practical tools to identify critical vulnerabilities and optimize recovery strategies when facing various disturbance events. Through simulation analysis of China's railway network, this research offers empirical evidence to support infrastructural planning and emergency management decisions that enhance overall network resilience. To establish a clear analytical framework for this study, we define three key resilience dimensions that will be used throughout this paper:  
1. Accessibility: A measure of the railway network's ability to maintain connections between stations when subjected to disturbances. Accessibility quantifies how effectively the network preserves paths between station pairs, maintains clustering coefficients, and sustains overall network efficiency during disruption events. 2. Invulnerability: A measure of the railway network's resistance to cascade failures when critical components are compromised. Invulnerability examines how well the network withstands progressive degradation when initial failures trigger subsequent component failures through load redistribution mechanisms. 3. Restorability: A measure of the railway network's capacity to regain functionality following disruptions. Restorability evaluates the efficiency of different recovery strategies in reinstating network performance, particularly the relative effectiveness of targeted versus random approaches to component restoration. These three dimensions collectively provide a comprehensive characterization of railway network resilience, addressing both the network's capacity to withstand disturbances and its ability to recover from them. By analyzing these dimensions across different types of disturbance events, this study aims to identify critical vulnerabilities and optimal recovery strategies for China's railway transportation system. The full text is divided into five sections, and the content of each section is arranged as follows: Section 1 introduces the research background and significance of railway network resilience in China, and outlines the key challenges currently faced in this field. Section 2 collates and makes a summary analysis of the domestic and international research status on the resilience research into transportation networks and the complex structure analysis of transportation networks. Section 3, based on the operational characteristics of China's railway network and existing resilience studies, develops a comprehensive resilience evaluation model. This includes the construction of an index system encompassing accessibility, invulnerability, and restorability.