Abstract: Against the background of intelligent transformation and high-quality development in the shipbuilding industry, large models represented by large language models (LLMs) and multimodal large models have gradually become core technologies driving the intelligent upgrading of the entire ship life cycle. This paper systematically reviews the research progress of integrating large model technologies with the shipbuilding industry, and clarifies the technical principles, capability boundaries and existing limitations of large models in engineering applications. By summarizing the development route of artificial intelligence and the underlying mathematical logic of large models with Transformer as the core architecture, this study analyzes the key capabilities of large models in natural language interaction, multi-step reasoning, multimodal understanding, few-shot learning and code generation, as well as inherent defects such as hallucinations, weak causal reasoning and poor real-time performance.According to the engineering characteristics and safety requirements of the shipbuilding industry, this paper proposes three basic application principles for large models: 1) ntroducing ship industry knowledge as constraints to improve output reliability; 2) taking large models as planning engines to realize the decomposition of complex tasks and the collaboration of professional tools; 3) and building multi-agent systems with large models as the decision-making center to support autonomous decision-making and closed-loop execution. Guided by these principles, this paper sorts out and evaluates the application cases of large models in four typical scenarios: ship design, ship manufacturing, operation and maintenance, and navigation decision-making. It is found that the knowledge-constrained scheme is suitable for standardized scenarios such as rule compliance and design consulting; the planning engine framework is efficient in complex task decomposition; and the agent system shows unique advantages in autonomous decision-making and multi-ship collaboration.Furthermore, this paper discusses the technical bottlenecks restricting the large-scale engineering implementation of large models in the shipbuilding industry, including data quality and governance, real-time response for navigation control, decision interpretability and safety verification. On this basis, a step-by-step integration idea of "technology alignment–scenario adaptation–system implementation" is put forward to promote the reliable landing of large models in the whole life cycle of ships. Finally, the future development trends are prospected from the aspects of multimodal fusion, causal reasoning enhancement, human-in-the-loop reinforcement learning and digital twin-driven simulation verification. The conclusions and methods of this paper can also provide theoretical references and practical guidance for the intelligent transformation of other complex industrial fields such as aerospace, astronautics, energy and electrics.