The importance and difficulty of large-scale quantitative analysis of fishing vessel footprints and fishing intensities with high resolution have received more and more attention and recognition in fishery research. In this study, a framework for identifying and analyzing fishing footprints and activities based on AIS data is proposed. The first step is the AIS data pre-processing. Through the screening and cleaning of AIS data, noise data such as non-fishing vessel trajectories are removed to retain only fishing vessel trajectories. The second step is the behavior detection of fishing vessels. Based on the proposed speed identification method, the trajectory of a fishing vessel is divided into three states: Fishing, stationary, and sailing. The third step is the calculation of fishing vessel activities. An adjacent point mean method is proposed to calculate the fishing time of each fishing point. In addition, two simple and efficient quantitative indicators, namely unit cell activity frequency (UCAF) and unit cell fishing time (UCFT), are proposed to quantify the activities of fishing vessels. The last step is the spatial and temporal analysis of fishing activities. The global Moran's index and Getis-Ord Gi* index are used to quantitatively describe the global spatio-temporal distribution and local spatio-temporal hotspots of fishing activities. Based on >130 million AIS data from 2015 to 2016, the footprints of fishing vessels in the waters of China's exclusive economic zone (EEZ) were analyzed and the 0.01^° × 0.01^° high-resolution fishing vessel activity distribution maps and fishing time distribution maps were generated. It is found that the footprints of fishing vessels in China's EEZ waters have a positive spatial correlation, and the areas with high fishing intensities are clustered in the coastal waters of Shandong Province, Zhejiang Province, Fujian Province and Guangdong Province. The research results can be used for fishing area identification, fishery policy making, and sustainable fisheries development.