The prediction of fishing conditions is to predict the locations of fish schools and the abundance of fish in those areas. With knowledge of future fishing conditions, managers can formulate effective strategies and fishermen can cut down their resource consumption in the fishing process. This study starts with the remote sensing data of the marine environment and automatic identification system (AIS) fishing vessel trajectory data, analyzes the distribution of fish schools, and predicts future fishing conditions. According to different operation methods, fishing vessels can be divided into many types, such as purse seine, gillnet, trawl, and stow net types. It is of great significance to predict the future operation areas of fishing vessels equipped with different fishing gears and carry out fine management. The traditional single-task learning can achieve individual predictions for each fishing gear, but it cannot capture the interaction of various fishing gears. Therefore, this study proposes a multi-task prediction method based on a spatiotemporal neural network of ocean remote sensing data and AIS fishing vessel trajectory data. This method can capture the interaction of the fishing gears in addition to conducting separate predictions for each fishing gear. The prediction accuracy is further improved by embedding environmental remote sensing data such as ocean temperature and salinity into the model. Experiments are conducted on the data set of AIS fishing vessel trajectories in Zhejiang sea area, China, and the results prove the superiority of this method to the classical method and the latest onebased on ocean remote sensing and AIS trajectory inpredicting the distribution of fish schools.