Studying the identification and resilience optimization of critical nodes in urban rail transit networks can prevent network interruptions and enhance the network’s ability to resist risks. This article adopts a TOPSIS model based on entropy weighting method to design a node importance evaluation approach that considers both local and global network structure information. Furthermore, under the constraint of financial budget and considering the comprehensive interests of different stakeholders in the network, a network resilience optimization model is constructed. Using Beijing’s subway as a case, when the network is subjected to sequential-deliberate attacks until collapse, the node coupling importance evaluation method proposed in this article reduces the number of attacked nodes by 4.39%, 2.59%, and 0.52% respectively, compared to Degree Centrality, Closeness Centrality, and Betweenness Centrality. This approach helps identify and maintain critical network nodes in different areas, ensuring the stability and safe operation of urban rail transit networks. Compared to the existing network, the optimized network exhibits a 3.36% improvement in flexibility, with an increase in the number of attacked nodes by 0.26% and 14.21% under random and sequential-deliberate attacks until collapse, demonstrating that the proposed elastic optimization method effectively improves the risk resilience of urban rail transit networks.