In order to do a good job of emergency management in the airport sector, strengthen the construction of the emergency response system, and improve the emergency response capability, a decision support method based on hybrid reasoning was proposed for the disposal of airport emergencies. Firstly, the ontology model of airport emergencies was constructed by abstracting the emergencies and the disposal process in the airport based on the actual scenarios and official documents. Secondly, the hybrid reasoning combining rule-based reasoning and case-based reasoning was introduced for case retrieval, and case representation was performed for the constructed ontology model to construct a case database. Lastly, the retrieval results are corrected using a feature weighting algorithm for attribute trade-offs, and the attribute parameters were adjusted using a neural network-based weight parameter optimization strategy. The advantages of the Bert+LSTM combination in this task scenario were verified by comparing it with commonly used deep learning models, and the final example proves that when an emergency occurs, the model can focus on the emergency itself, refer to historical cases and disposal standards, and obtain a structured data that comprehensively describes the information and disposal measures of the emergency, which provides support for emergency disposal decision making.