## 5 Conclusions  
Based on complex network theory, this paper constructs a model for key node identification and resilience study in RWCTN and solves the problem of complex network resilience analysis in the context of RWCTN. Specifically, this study first analyzes the network structure and characteristics of RWCTN and establishes a static topological model and diagram of CTN involving maritime and railroad transportation based on complex network theory. It then tackled vulnerability and resilience from network resilience theory, identifying key nodes in the network by developing a node importance evaluation model. Using major domestic railway and waterway coal transportation channels, including the Daqin Line, Shenshuo-Huanghua Line, Taiyuan-Jiaozuo Line, Longhai Line, and Beijing-Kowloon Line, and taking the major cities, coal mines, railway hubs, important coastal ports, and border crossings that these lines pass through and arrive at as nodes, the coal-water-iron transport network topological diagram was constructed using the latitudes and longitudes of each node. The proposed GRE-TOPSIS method is used to identify critical nodes. The effectiveness of the algorithm is verified through deliberate and random attack simulation experiments based on the metrics of network efficiency, network connectivity, maximum connectivity subgraph, and natural connectivity. The main contributions of this paper include:  
* Firstly, introducing the Grey Relational Analysis and entropy weighting method, integrating existing methods to propose an improved GRE-TOPSIS method, and enhancing the objectivity of the TOPSIS method's calculations;
* Secondly, breaking through the analysis of single-medium network performance and studying the iron-water combined transport network, aligning more with China's coal transportation mode;
* Thirdly, this research, based on China's real coal transportation network, evaluates and ranks 84 nodes comprehensively. By analyzing network performance changes through deliberate and random attacks, the effectiveness of the method and its feasibility for application in real-world complex networks have been validated. The main conclusions and insights derived from this study are:  
* Firstly, by constructing a compound network for coal-water-iron transportation, the main coal transport network-related indicators were obtained, including network degree, betweenness centrality, and other topological structure characteristics, as well as network efficiency, network connectivity, and natural connectivity performance indicators. These indicators reveal connectivity issues in the iron-water transportation network, overloads on important nodes, and can guide future construction, development, and planning of iron-water transportation. * Secondly, the entropy method objectively determines weights based on indicator variance, while the gray relational analysis evaluates the similarity or dissimilarity of development trends between factors. This paper proposes combining gray relational analysis and entropy weighting to calculate weights, enhancing the objectivity of the GRE-TOPSIS method in selecting key nodes and achieving a more accurate and objective calculation of node importance. * Thirdly, through the comprehensive evaluation system for key nodes in the network, after comprehensive evaluation and ranking of key nodes in the network, it was found that nodes like Yuanping (Node 76), Macheng (Node 36), and Suning (Node 28) may not rank highly based on topological characteristics, but due to their strong freight capacity and connections to central and western parts, they also rank highly in comprehensive evaluations. GRE-TOPSIS can identify these key nodes, providing an essential theoretical reference for targeted protection in practical applications. * Finally, network performance analysis reveals that network efficiency and maximum connectivity subgraphs, as well as network connectivity and natural connectivity, have similar trends, especially under deliberate attacks.