Deep learning models have achieved great success in the field of ship detection, but these models often require a large amount of computing and storage resources, and are not suitable for some resource-constrained situations. To solve the above problems, we propose a lightweight Swin-YOLOFormer ship detection method. First, in terms of the backbone network, the Swin transformer lightweight model is introduced to reduce the redundancy parameters of the backbone network. Second, in the feature fusion network, an improved ghost-efficient long-range attention network-hierarchy module is proposed to extract features and reduce the burden of model parameters. Finally, in order to prevent feature loss, an improved SPPCPSC module is proposed to enhance the feature of receptive field. Through experimental verification, compared with the YOLOv7 benchmark model, the number of proposed model parameters was reduced by 66.05% to 13.501 M, which not only accelerated the model training speed but also reached 97.81% accuracy. The results show that the proposed method achieves model lightweight and maintain high precision.