In aviation domain， stable communication between air units and ground units requires unstructured text messages be transformed into structured information in a fixed format for transmission. Although the existing methods use the instruction-following capabilities of Large Language Models （LLMs） to perform such transformation from text into structured data， they ignore the inherent hallucination and lack of interpretability of LLMs — which pose serious threats to information security. Therefore， a Message Consistency Parsing Framework for the Aviation domain （MCPFA） was proposed to integrate model self-reflection and human-machine collaboration to ensure semantic fidelity and operational safety during the parsing process systematically. First， a Consistency Verification Method based on Self-Reflection （CVMSR） was developed to establish a progressive validation mechanism across three dimensions： syntax， semantics， and pragmatics， so as to realize dynamic verification and correction of the structured output. Second， an Intelligent Aviation Message Human-Machine Collaboration Inspection System （IAMHCIS） was designed to use confidence awareness and cross-layer consistency analysis to trigger expert intervention precisely and incorporate human feedback into model iteration， so as to form a closed-loop optimization. Finally， the framework was evaluated comprehensively through systematic experiments. The results demonstrate that MCPFA outperforms the existing methods significantly in both consistency judgment and correction tasks， identifying and fixing critical errors caused by term confusion， unit misinterpretation， or regulation violation effectively. Furthermore， the framework exhibits strong generalization capability on unseen message types. Importantly， after integrating expert knowledge， performance gains continuously， confirming the proposed framework’s growability and practical value， and the proposed framework provides a new accurate， secure， and interpretable way for LLM application in aviation and other safety-critical domains.