In shipbuilding, thin-plate production lines are characterized by complex processes, diverse equipment, and heterogeneous data sources. Traditional digital twin modeling relies heavily on manually defined interfaces and attributes, resulting in low efficiency and limited consistency. This paper proposes a generative digital twin modeling method tailored for thin-plate production lines. The method constructs five core ontologies—covering process units, equipment, attributes, sensing-control units, and functional behaviors—to establish a unified semantic structure. Furthermore, a mapping mechanism from ontology to the digital twins definition language (DTDL) is designed to enable standardized representation of domain knowledge. A large language model is then introduced to leverage its semantic understanding and generative capabilities, automatically transforming natural language descriptions into structured models compliant with the DTDL v3 specification. Semantic consistency validation is performed to ensure the correctness of model logic and constraints. Experimental results demonstrate that, compared with manual approaches, the proposed method improves modeling efficiency, semantic accuracy, and attribute coverage by approximately 79.6%, 9.4%, and 28.5%, respectively—significantly enhancing the automation and standardization of digital twin modeling.