## 1 Introduction  
### Background  
With the advancement of intelligent shipping, traditional maritime supervision systems such as ship automatic identification systems, ship traffic management systems, very high frequency (VHF) wireless, and ship video supervision systems have become inadequate to meet the requirements of intelligent supervision. These systems are unable to achieve the safety supervision purpose of wide area coverage, intelligent decision-making, and rapid response [1, 2]. In particular, with regard to ship violations, on-site boarding is still necessary for inspection. The current practice of comparing the automatic identification system (AIS) dynamic information of the inspected ship with the AIS information on the maritime supervision platform through manual identification, to identify the ship as abnormal or violating if it does not match, is time-consuming and inefficient for timely and accurate maritime supervision [3, 4]. Moreover, challenges such as irregular and renamed ship names, diverse configurations of ship AIS equipment, multiple ships with one nine-digit code or one ship with multiple nine-digit codes [5, 6], and irregular encoding of ship nine-digit code information also present significant obstacles to the precise detection of ship violations [7, 8]. Currently, research on identifying ship's illegal behaviors primarily focuses on ship traffic management. [PERSON] and [PERSON] [9] introduced a traditional method for analyzing and discriminating AIS ship data to identify illegal coding problems of ship AIS equipment, with a single recognition target and low performance. [PERSON] et al. [10] proposed a new method for automatically identifying ship collision avoidance behavior from ship trajectories using AIS trajectory data, and the data source is relatively small and fails to consider more comprehensive factors, resulting in insufficient reliability and accuracy of identification [11]. [PERSON] et al. [12] elaborated on the attribute characteristics of key ship data in maritime data according to different ship uses and stowage methods, focused on the data attributes themselves and failed to attempt to explore the association relationships between attributes. [PERSON] et al. [13] proposed a framework for port sensing and computing based on maritime big data. While this method uses multi-source data, such as ship GPS tracks, ship attributes, port geographic information, and port facility parameters to construct maritime big data, it still relies on system-level data analysis. [PERSON] et al. [14] used spatial clustering to identify accident-prone areas, analyze the causes of accidents, and develop risk prevention and control strategies based on maritime accident data, but did not consider other dimensions to make comprehensive assessment decisions. [PERSON] et al. [15] proposed an object-oriented Bayesian network model to quantitatively assess the risk of maritime accident scenarios in ice-covered Arctic waters concerning human and organizational factors. Similarly, [PERSON] and [PERSON] [16] reviewed visualization for multifaceted scientific data and visual analysis techniques but did not use knowledge graphs as visual objects. This analysis of the literature shows that the identification of violations in multi-source maritime ship data is predominantly based on the manual matching of information. Research methods for processing before data analysis are also mostly conventional, and the source data used for data analysis are relatively homogeneous, failing to analyze the behavior of ships comprehensively. Most research on maritime mass data focuses on the attribute characteristics that can be directly reflected by the data themselves, failing to explore the potential behavior of ships under big data. Although research on ship applications based on maritime data is relatively abundant, the functions are often similar and discrete, leading to issues such as the homogeneity of ship behavior analysis and inconsistency of analysis points.