The work aims to effectively analyze and explore the space-time trajectory behavior patterns of ocean vessels, improve the efficiency and quality of vessel trajectory clustering, and better detect abnormal behaviors of real vessels. In allusion to existing problems in current vessel trajectory data research, such as insufficient utilization of multidimensional feature information, low detection efficiency, poor detection accuracy, etc., a high accuracy and multi-dimensional feature identification method for vessel abnormal trajectory was proposed. Firstly, random forest classifier was used to evaluate the importance of multidimensional features and construct the optimal combination of trajectory features. Then, a dimensional-density reduction clustering method was proposed to combine T-SNE and DBSCAN models. By constructing feature selection layer and unsupervised clustering layer, the nonlinear relation of data elements could be extracted efficiently and the clustering parameters could be selected intelligently. Finally, the cluster feature vector was constructed according to the clustering results, and the distance threshold was calculated to distinguish the trajectory similarity, and the trajectory anomaly detection model was constructed. With UCI datasets as examples, the F1 score of this method could reach 0.904 8, 0.953 4, 0.821 8 and 0.662 7 for datasets with 4, 13, 30 and 64 dimensional features, and many clustering indexes were superior to DBSCAN, K-means and other common clustering algorithms. The results show that this method can effectively extract multi-dimensional feature structure of data, realize clustering parameter self-adaptation, make up for the problem that parameters are difficult to be determined in density clustering, and effectively realize the identification of multiple types of ship trajectory anomalies.