Abstract: To address the difficulty in quantitatively measuring and recognizing the similarity of multi-ship encounter scenarios, a similarity-based recognition method for multi-ship encounter scenarios based on topological graph sequences is proposed. First, multi-ship encounter scenarios are extracted from Automatic Identification System (AIS) data and represented using a topological graph sequence-based model that characterizes ship interaction relationships. Then, a two-stage similarity recognition algorithm is designed to calculate the similarity between graph sequences and identify similar encounter scenarios. Taking the waters of Ningbo-Zhoushan Port as a case study, 2,898 multi-ship encounter scenarios are extracted from one month of AIS data, and two typical scenario types with higher proportions are selected for experimental validation. The recognition results are comparatively analyzed based on encounter feature parameters. Experimental results show that the identified similar scenarios exhibit dynamic evolution characteristics highly consistent with those of the original scenarios, and the proposed method can effectively recognize multi-ship encounter scenarios with similar encounter relationships. This demonstrates the feasibility and effectiveness of the proposed method for similarity measurement of multi-ship encounter scenarios. The findings can provide a reference for collision avoidance decision-making and encounter risk analysis in multi-ship encounter scenarios.