###### Abstract
Inappropriate spatial scales can lead to the overestimation or underestimation of fishing pressure in fishing grounds, resulting in distortions or even erroneous outcomes. Spatial information extracted from AIS data must exhibit good spatial similarity with the spatial distribution of fisheries resources at an appropriate spatial statistical scale. Therefore, a comprehensive exploration is necessary to assess the reliability of the extracted fishing ground spatial information, considering both the spatial topology and the spatial consistency of information at different spatial scales relative to fishing production data. This exploration aims to provide a scientific basis for the effective utilization of the AIS in fisheries management. High-sea light purse seining, a method employing lights to attract and capture small pelagic fish (such as mackerel, sardines, etc.) or cephalopods, constitutes a significant component of the world's high-sea fisheries [16]. In 2017, mainland China's fishing vessels initiated light purse seining activities in the Arabian Sea. Within this fishing area, purse seine catches are predominantly composed of squid, followed by mackerel. Despite its importance, this fishery area has been the subject of limited studies on its resources. Several investigations have been conducted on the fishery distribution and the relationship between the marine environment and primary fishing targets, namely, squid and mackerel [17; 18; 19]. Additionally, fishery forecast models have been developed [20] in an effort to comprehend the spatial and temporal distribution patterns of resources in this region. Nevertheless, there remains a dearth of comprehensive knowledge concerning the status of fishery resources and fishing pressure in this area. Therefore, understanding the fishing activities of vessels in this region is crucial for the sustainable utilization of fishery resources. Notably, the literature has primarily relied on logbook data for analysis. However, logbook data cannot be publicly accessed, and may be susceptible to underreporting, particularly in high-sea regions, potentially leading to a misinterpretation of our understanding [21]. Furthermore, statistics on high-sea purse seine data suffer from poor timeliness, often being delayed by up to half a year. This delay limits our ability to promptly grasp the production and harvesting status of fishery resources in the region. To provide a real-time and dynamic understanding of the operational status of light purse seine fishing in the Arabian Sea, this paper utilized the trajectory information of mainland China's light purse seine fishing vessels in 2021 and 2022. By employing three data analysis methods--data mining, KDE, and HSA--this study extracted spatial information such as fishing intensity, fishing hotspots, and fishing vessel operations. By mapping and conducting spatial similarity tests with concurrent production and commercial catch data,this study compared the topology and spatial consistency of the three calculated spatial information types. The optimal spatial information was then used to analyze the spatial distribution characteristics of purse seine fishing vessels in the Arabian Sea. Finally, this paper explores the application and limitations of AIS data in managing and supervising fishing vessels in the region. This research aims to provide a scientific foundation for the application of the AIS in fishery management and research in the Arabian Sea.