The trajectories of fishing vessels in the field of marine fisheries are spatiotemporal and non-stationary. Considering the problems of insufficient data extraction and low recognition accuracy in the current operation mode recognition methods for fishing vessels, an operation mode recognition model for fishing vessels, i.e., 1DCNN-SAGRU, is proposed. This model is based on the one-dimensional convolutional neural network (1DCNN) and the gated recurrent unit (GRU) network with self-attention. The model uses 1DCNN and GRU to fully extract local spatial features and temporal dependencies of the trajectory data of fishing vessels. In addition, the self-attention mechanism is introduced to strengthen the model’s ability to focus on key information. Finally, the dropout method and the RAdam optimizer are introduced to improve and optimize the model, which can prevent the overfitting of the model, speed up the convergence, and raise the output accuracy of the network. Experiments and analysis show that compared with the accuracy of other comparative models, the accuracy of this model can be improved by up to 4.4 percentage points. This indicates that the model can more accurately identify the trawl, purse seine, and gill net operations of fishing vessels, which is conducive to strengthening the regulatory capacity of fishing vessels and the protection of fishery resources.