为切实做好机场部门应急管理工作，加强应急处突体系建设，提高应急处突能力，提出了一种基于混合推理的机场突发事件处置决策支持方法。首先依据实际场景和官方文档抽象机场中的突发事件及处置流程，构建机场突发事件本体模型；其次，引入规则推理和案例推理相结合的混合推理进行案例检索，针对构建的本体模型进行案例表示并构建案例库；最后，借助属性权衡的特征加权算法对检索结果进行修正，并采用基于神经网络的权重参数优化策略调整属性参数。通过和常用深度学习模型进行对比，验证了Bert+LSTM组合在该任务场景下的优势，最终实例证明，当突发事件发生时，模型能够关注突发事件本身，参考历史案例和处置标准，得到一份全面描述突发事件信息和处置措施的结构化数据，为突发事件的处置决策提供支持。

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.