In order to accurately identify the ship’s identity, a ship identity recognition model based on hierarchical attention Siamese network was proposed. By combining the long-term and short-term memory of temporal attention and the multi-scale convolutional network, the ship trajectory has been characterized at the level of temporal and semantic information, and by using  an improved Siamese network to calculate the dissimilarity score between the ship trajectory characterization vectors as a basis for determining the ship identity. To verify the effectiveness of the proposed model, the performance of the proposed model and the commonly used machine learning and deep learning models were compared and analyzed in terms of the ship trajectory data of Xiamen port and nearby waters. The results show that the proposed model can obtain good generalization performance of ship identity recognition on a small-scale data set, with a F₁ score of 0.8971 on the test set, while the commonly used machine learning models can only achieve a F₁  score of 0.7774 on the same test set, which indicates that the proposed model can meet the needs of applications related to ship identification and anomaly detection.