###### Abstract  
AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision. space-based AIS; ship classification; integrated learning; data mining +
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in Figure 1. Furthermore, these ships broadcast few message5, which makes it difficult to identify their types through message5. Except for the problem of the types of ship being unknown, the types of ships may be mislabeled for various reasons [6,7], which often relate to violations, such as smuggling and illegal fishing [8]. These security and law enforcement issues put forward higher requirements for maritime traffic supervision. If ship types can be obtained from historical AIS data, the corresponding prior knowledge of a certain type of ship can be used for maritime traffic management. Thus, accurate identification of ship type is helpful in enhancing the maritime situational awareness of the related departments and is of great value in various areas, such as maritime surveillance, camouflage identification, ship behavioral pattern mining, and anomaly detection. Existing ship classification methods, based on AIS data, generally consider static and dynamic information. For static information, [PERSON] et al. [9] use KNN (K-NearestNeighbor) to classify ships based on tonnage, length, and width in Indonesian waters and achieved an accuracy of up to 0.83 on six categories. [PERSON] et al. [10] use Random Forest for static information and achieve an accuracy of 0.865 on a three-classification task. For dynamic information, [PERSON] et al. [11] study the ships near the Ieodo Marine Research Station, and infer ship types by comparing the flag state of ships and the distribution of their corresponding trajectories with those of type-unknown ships. [PERSON] et al. [12] use decision trees to identify fishing boats and achieve an accuracy of 0.8 and an F1 score of 0.7. Moreover, through conducting comparative experiments, they attempt to extract motion features as possible data. [PERSON] et al. [13] extract the COG (course over ground), ROT (rate of turn), and global features of vessels in the sea near Shantou, China, and use logistic regression to distinguish fishing and cargo vessels, achieving an accuracy of 0.923. [PERSON] et al. [14] propose a multi-view feature fusion network that combines the CAE (convolutional auto-encoder) and the Bi-GRU (bidirectional gated recurrent unit) network to classify ships. They realize an accuracy of 95.51% and 94.24% in Luotou Channel and Qiongzhou Strait, respectively. [PERSON] et al.