In the current fine-grained classification task of ships, approaches that rely solely on single image data can only classify by extracting the image features of the target. However, they struggle to capture the complex relationships between the ship's main body and its components, thereby limiting recognition accuracy and results in poor generalization. A data- and knowledge-driven fine-grained classification method, termed DKSCN, is proposed for ships. The object detection network is utilized to detect the ship's main body and its key parts. By designing a graph convolutional network and integrating expert knowledge, a semantic knowledge graph is established to capture the relationships between the ship's main body and its key components. During classification, domain knowledge is incorporated to guide the data-driven process. Comparative experimental results on a self-constructed dataset demonstrate that this method not only addresses the limitations of single data-driven models but also improves classification accuracy.