AIS data refers to the vessel’s motion trajectory information obtained through the AIS system. Mining AIS data can provide insights into the vessel’s motion patterns, navigation routes, docking locations, etc. However, outliers generated during the AIS data collection can have a negative effect on clustering and other tasks. Therefore, outlier detection on AIS data before mining is necessary. However, when there are a large number of outliers in AIS trajectory data, a significant decrease occurs in the accuracy of most outlier detection algorithms. To address this issue, this study proposes a trajectory outlier detection based on center shift (CSOD). The CSOD algorithm encourages data points to move towards the center of their K-nearest neighbor (KNN) set, making each data point closer to typical data and effectively eliminating the influence of outliers on clustering. To validate the effectiveness of the proposed algorithm, the study conducts comparative experiments between the CSOD algorithm and several classical outlier detection algorithms using the AIS fishing vessel trajectory dataset in the Zhejiang sea area. The experimental results demonstrate that the CSOD algorithm outperforms the other algorithms in terms of overall performance.