Overfishing and illegal fishing have caused serious damage to marine ecology. With the development of the automatic identification system (AIS) on vessels, scholars have proposed plenty of algorithms based on AIS trajectory data to identify the operation types of fishing vessels. However, these algorithms ignore the temporal features of the trajectory. Therefore, this study puts forward the identification of operation type based on the sliding window and LSTM auto-encoder. Firstly, it utilizes the sliding window to extract trajectory features and then uses an LSTM auto-encoder to learn the temporal features and potential advanced features of trajectories. Finally, the Softmax classifier is embedded in the LSTM auto-encoder to jointly optimize the cost function, achieving the best classification. The algorithm is verified based on AIS trajectory data of fishing vessels in the Zhejiang sea area, China. The results show that the accuracy is 95.82%, which proves the effectiveness and reliability of the proposed algorithm. The algorithm can be used to assist in judging the operation type of trawl and purse seine.