Intelligent analysis of ship collision accidents via Low-Rank Adaptation-based fine-tuning of medium-scale Large Language Models

The rapid advancement of intelligent maritime accident analysis requires processing large-scale, multilingual data across wide geographic regions. However, significant challenges remain in objectively constructing Risk Influencing Factors (RIFs) and ensuring accurate information extraction with limited computational resources. To address these gaps, a framework for intelligent analysis of ship collision accidents based on Low-Rank Adaptation (LoRA) fine-tuning of medium-scale large language models (LLMs) with limited labeled data was proposed. A bilingual dataset comprising 503 ship collision accident reports was established, and the RIF ontology was derived using a Grounded Theory approach. Using 60 labeled samples, models with ≤ 8 B parameters were fine-tuned, achieving an F1 score of 94.11% on the most challenging accident RIF extraction subtask, surpassing base models by 34.82%. Then, the extracted information was transformed into a 1061-row ×24-column training data matrix via a semantic similarity model, enabling construction of a TAN-BN model. Finally, sensitivity analysis was conducted to identify key RIFs, and case studies were performed to evaluate model performance and validate the proposed framework. The research results showed that the proposed approach advances large-scale, cross-lingual intelligent maritime accident report analysis by improving accuracy and efficiency, reducing computational costs, and supporting reliable safety management decisions. • An intelligent analysis framework is developed for comprehensive, data-driven ship collision risk assessment. • Grounded Theory is used to create a structured, accurate ontology for Risk Influencing Factors (RIFs). • Fine-tuning medium-scale LLMs with LoRA and few-shot samples improves F1 score by 35.15% for RIF extraction. • Sensitivity analysis and case studies validate the framework’s effectiveness in real-world ship collision risk assessment.