A better understanding of the behavior of offshore fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been suggested as a novel tool to explore the movements of fishing fleets in near-real time. The fishing behavior and effort of vessels determined by vessel speed data obtained from AIS could assist in fishery resources analysis. In this study we used AIS data extracted from exactEarth Shipview^TM and fishing log data of longline vessels in the Western and Central Pacific Ocean; both types of data were collected from October to November 2017 and were analyzed together in order to establish a vessel status recognition model by evaluating the speed and heading characteristics of longline fishing vessels. The fishing effort model was defined, and the fishing intensity information of the fishing grounds was calculated based on the output of the fishing activity identification model. In order to test the fishing effort rationality data extracted from AIS, the spatial correlation coefficients of the fishing intensity obtained from AIS data mining and the catch per unit effort (CPUE), the total number of tuna, the total catch weight, and hook numbers were calculated. Our results indicated that the speed of longline vessels was mostly between 3 to 9 knots while fishing. The heading ranges of longline vessels were between 0 to 10° and 300 to 360°. The fishing activity was classified based on the speed and heading of vessels; the accuracy of the fishing vessel status classification was 68.29%. The spatial correlation of fishing intensity between threshold classification and logging was high (>0.96, <0.000001). The spatial distribution characteristics of the fishing intensity based on AIS were similar to the actual ones but lower than later. The spatial correlation coefficients of the fishing intensity obtained from AIS and CPUE data, the total number of tuna, the total catch weight, and hook numbers were all greater than 0.62 (<0.00001). Data on the fishing intensity of fishing vessels obtained from AIS could provide high-resolution information for scientists and decision makers and could be used as alternative data in fisheries stock assessment and management.