空中交通管制运行安全正面临多元风险致因导致潜在危害的问题。为解决多元风险引发的管制不安全运行问题，基于对管制不安全运行事件报告的全面分析，对管制运行安全风险信息和潜在规则进行挖掘；通过对隐含狄利克雷分布（latent Dirichlet allocation, LDA）风险主题发现模型挖掘的风险主题和关键词进行分析，明确管制运行风险主题及不同风险主题间相互演化规律；针对风险主题关键词，构建了民航管制运行领域的基于BERT（bidirectional encoder representation from Transformers）模型的语义网络，分析风险主题相互关联的风险特征，得出风险主题间潜在关系，可为关键词间关联度的量化提供一定理论依据；促进民航管制运行安全风险的数字化呈现的发展，挖掘管制不安全信息，为准确感知管制运行风险奠定基础。

In order to address the challenges posed by multifaceted risk factors in air traffic control operations, a comprehensive analysis of unsafe operational incident reports was performed to extract risk-related information and identify underlying patterns. The latent Dirichlet allocation (LDA) model was utilized to uncover key risk topics and associated keywords, and the evolutionary relationships among different risk themes were systematically analyzed. A semantic network for the civil aviation air traffic control domain was constructed using the bidirectional encoder representation from Transformers(BERT) model to examine the interconnections and potential dependencies among risk topics. This network provides a theoretical foundation for quantifying the association between keywords. The findings indicate that the proposed approach enhances the digital representation of safety risks in air traffic control operations. It is concluded that the results offer valuable insights for advancing risk assessment and mitigation strategies in civil aviation air traffic control systems. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.