In critical task scenarios such as aeronautical data link command parsing in the military domain， information extraction systems must achieve both high accuracy and structural consistency to ensure the safety and reliability of mission execution. However， the traditional extraction methods based on rules or few-shot example prompts often suffer from severe extraction results’ hallucinations when dealing with complex semantics， flexible syntax， and highly constrained structures. To address this issue， a Retrieval-Augmented Generation Information Extraction （RAG-IE） algorithm based on Large Language Model （LLM） was proposed by combining retrieval-augmented technology with information extraction technology. First， a meta-language library oriented to the target information types was constructed to provide standardized extraction templates. Then， efficient template retrieval was achieved based on an inverted index by combining keyword matching with semantic expansion strategies. Finally， the prompts were designed to guide the LLM to convert natural language into structured information， thereby enabling highly controllable and accurate information extraction of natural language commands in aviation data link scenarios. Experimental results show that this algorithm is superior to the existing information extraction algorithms， reducing the hallucinations in the information extracted by LLMs greatly. Meanwhile， this algorithm has strong domain independence and can be extended to other scenarios requiring highly reliable extraction by reconstructing the meta-language library， providing a generalizable technical pathway for building intelligent and verifiable information processing systems.