Due to the difficulties of complex backgrounds and large-scale differences between objects during the process of ship multi-object tracking in sea-surface scenarios, an improved CSTrack algorithm for ship multi-object tracking is proposed in this paper. Firstly, as violent decoupling is used in the CSTrack algorithm to decompose neck features and cause object feature loss, an improved cross-correlation decoupling network that combines the Res2net module (RES_CCN) is proposed, and thus more fine-grained features can be obtained. Secondly, to improve the tracking performance of multi-class ships, the decoupled design of the detection head network is used to predict the class, confidence, and position of objects, respectively. Finally, the MOT2016 dataset is used for the ablation experiment to verify the effectiveness of the proposed module. When tested on the Singapore maritime dataset, the multiple object tracking accuracy of the proposed algorithm is improved by 8.4% and the identification F1 score is increased by 3.1%, which are better than those of the ByteTrack and other algorithms. The proposed algorithm has the advantages of high tracking accuracy and low error detection rate and is suitable for ship multi-object tracking in sea-surface scenarios.