A Knowledge Graph-Driven Framework for Complex Vessel Behavior Recognition and Frequent Sequential Pattern Mining Using AIS Data

Understanding complex vessel behaviors in port waters is important for maritime traffic supervision, navigation safety management, and intelligent maritime decision-making. However, existing AIS-based studies often focus on isolated behavior recognition or trajectory-level analysis, with limited integration of vessel attributes, navigation scenarios, motion states, and temporally organized behavioral processes. We develop a knowledge graph-driven framework for complex vessel behavior recognition and frequent behavior sequence pattern mining using AIS data. BehaviorEvents are constructed from continuous-navigation segments and integrated with water-area scenarios, motion states, vessel attributes, and temporal relationships to form a unified semantic representation. Based on this representation, interpretable semantic rules are used for event-level complex behavior recognition, while PrefixSpan is applied to Scene–SpeedState–TurningState token sequences to discover recurrent multi-event behavior patterns. Independent expert evaluation, semantic ablation, and sensitivity analyses are used to assess recognition credibility, contextual semantic constraints, and robustness, while a vessel-level Discovery–Validation strategy evaluates the reproducibility of frequent patterns. Experiments on AIS data from Xiamen Port waters involve 16,400 vessels, 239,877 continuous-navigation segments, and 2,457,965 BehaviorEvents, of which 624,561 match at least one predefined semantic rule or candidate condition. Independent expert evaluation of R1–R7 yields a macro-average confirmation rate of 92.11% and a Cohen’s κ of 0.746. Semantic ablation shows that Scene and VesselTypeClass provide important contextual constraints on broad motion-based rule activations, while sensitivity analyses indicate that the main recognition results remain stable under perturbations of motion-state, duration, and temporal-segmentation parameters. From 75,144 valid compressed behavior-token sequences, PrefixSpan identifies recurrent patterns involving medium-speed transit with course adjustments, low-speed–stop combinations, and maneuvering-related behaviors. The dominant Top-20 patterns showed substantial overlap and broadly consistent ranking across the vessel-level Discovery and Validation subsets, with a Jaccard overlap of 0.9048 and a Spearman rank correlation of 0.9654. Comparative evaluation with a normalized relational representation further shows equivalent analytical results, while the knowledge graph provides explicit organization of semantic relationships, temporal paths, and event-level traceability. These results indicate that the proposed framework provides a unified and interpretable semantic basis for connecting event-level complex vessel behavior recognition with sequence-level frequent behavior pattern mining in complex port environments.