Abstract: In order to provide relevant information for natural resource management, impact assessment and marine spatial planning, high-resolution estimation of longline fishing gear is needed. Because the use of satellite vessel monitoring system (VMS) data is limited by data access and receiving offshore ship location information. In this paper, based on the data of automatic identification system (AIS) and logbook data of Chinese longline fishing from October to November 2017, a model of fishing detection based on support vector machine (SVM) learning method is established to classify the fishing and non fishing activities in the central and Western Pacific Ocean, to carry out longline fishing and draw fishing effort map in the Western and Central Pacific Ocean. Before constructing a fishing detection model, each AIS point was classified and pre-labeled as potential fishing and non-fishing events by an expert based on information on information on fisheries characteristics as obtained from literature, analysis of the tracks and logbook data. The performance of the fishing detection model was evaluated by the overall accuracy, precision, sensibility and specificity. Fishing intensities were computed from the known fishing positions and the estimated fishing positions were compared with correlation coefficient. The spatial correlation coefficient of fishing intensities and catch data was computed to quantify the extent to which the distribution of longline vessels describes tuna distribution. The results showed that the overall accuracy and the Kappa coefficient of the model training dataset were 95.24% and 0.9, respectively. The precision, sensibility and specificity were 94.74%, 93.44%, 96.56%, respectively. The overall accuracy and the Kappa coefficient of the model testing dataset were 93.85% and 0.87 respectively. The precision, sensibility and specificity were 93.49%, 90.67%, 95.91%, respectively. Then the constructed model was used to identify all the AIS data for 12 longline fishing vessels in the Western and Central Pacific in October and November 2017, with an overall accuracy rate of 83.3% and the Kappa coefficient was 0.67. The precision, sensibility and specificity were 82.33%, 88.32%, and 77.27%, respectively. The longline fishing distinct in October and November 2017 mainly located in 168°E-173°E, 12°S-18°S, and there were three obvious high fishing intensity areas on the map. The spatial distribution of fishing effort was significantly different between October and November. The distribution of fishing intensity in November was closer to the island than in October and the value of fishing intensity in October was lower than November. The fishing intensity information based on the SVM model and logbook data records was highly correlated (r>0.98). The spatial distribution characteristics of the fishing effort of the fishing vessel identified by the SVM model were similar to the known fishing positions. But the fishing intensities were calculated from known fishing positions was higher than that of estimated fishing positions. The spatial correlation coefficients of cumulative fishing effort and catch per unit of effort (CPUE), catch tail, catch weight and number of hooks were 0.68, 0.93, 0.93 and 0.94, respectively. The fishing capacity of fishing vessels based on AIS information mining can also be used as an alternative method for fishery resource analysis.