With inland waterways transitioning from linear to networked operation, accurately identifying critical segments is essential for optimizing resource allocation and enhancing system resilience. Existing methods have limitations in effectively identifying segments that play a decisive role in maintaining global connectivity. To address this issue, a community bridge-based method is proposed. Firstly, a weighted topological network is constructed using waterway class and length. Then, the Louvain algorithm is applied to divide the inland waterway network into multiple communities with strong internal connectivity, and edges connecting different communities are identified as critical segments. Finally, attack simulation experiments are conducted to evaluate the effectiveness of the proposed method. Taking the Jiangsu inland waterway network as a case study, the results show a maximum modularity of 0. 901, indicating a pronounced community structure characteristics, and the network can be divided into 18 communities. Currently, 46 critical segments are identified in the network. If all critical segments fail simultaneously, both relative network efficiency and the relative size of the largest connected component decrease by nearly 80%, validating the effectiveness of the identification method. After implementing the 2017—2035 and 2023—2035 waterway network upgrades, the community structure becomes more compact, and the number of identified critical segments decreases while the results remain consistent. The identified critical segments provide theoretical support for routine maintenance and safety supervision of inland waterways, strengthening navigational assurance to enhance network resilience.