Research on Intelligent Technology for Emergency Language Services Enhanced by Knowledge Graph

This study proposes a knowledge graph-enhanced intelligent framework for emergency language services. It employs a neural machine translation encoder to achieve crosslingual semantic alignment and constructs a dynamic emergency knowledge graph for structured information representation. By integrating retrieval-augmented generation, the system automates the conversion and response generation of multilingual disaster data. The core objective is to realize structured understanding of multilingual disaster information and automated resource matching. Experimental results confirm the system’s effectiveness in improving accuracy, matching efficiency, and scalability, offering a viable technical pathway for intelligent emergency response systems.