Objective Carrier-based aircraft launch and recovery operations are central to the combat capability of aircraft carriers. Their complexity and high risk make emergency decision-making a critical factor in ensuring flight safety.
Method To address the scarcity of special-condition cases and the difficulty of accumulating decision-making experience in carrier-based aircraft launch and recovery operations, this paper proposes an intelligent decision-support method based on hybrid vector–graph retrieval-augmented generation. First, a domain knowledge graph for carrier aviation is constructed based on authoritative professional books and relevant Wikipedia entries, forming a structured knowledge representation. Second, vector retrieval and graph retrieval are integrated, and reciprocal rank fusion is used to combine dual-channel evidence. Evidence deduplication, filtering, and structured presentation are further employed to improve retrieval quality. Finally, an evaluation benchmark containing 23 evaluation scenarios derived from real-world cases is constructed, and domain experts are invited to conduct multidimensional assessments.
Results Experimental results show that the proposed method outperforms baseline methods across all evaluation dimensions, achieving an 8% relative improvement over the base model. This verifies the effectiveness of hybrid retrieval-augmented generation for decision support in the specialized field of carrier aviation.
Conclusion This study provides a generalizable technical approach for intelligent decision support in knowledge-intensive and high-risk scenarios.
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