为深层次剖析民航事故诱因预防事故的发生，针对当前事故数据统计的不完整性并使事故致因分析具有可信度和深度，以2000年全球范围内的民航事故为样本采集事故报告数据并进行结构化处理，将事故分阶段类型等字段进行统计，并基于K-means算法从多方面进行民航事故风险要素聚类分析，对比了结构化与非结构化处理结果的差异，综合考虑多种风险要素对事故的共同影响，根据词云图中的关键致险因素和LDA（latent dirichlet allocation）主题建模识别文本数据中潜在主题的分布以及事故致因，从多个方面为今后的民航安全提供了有价值的参考信息和安全建议。

To conduct a deep-level analysis of the causes of civil aviation accidents for the purpose of preventing accidents, addressing the current incompleteness of accident data statistics, and ensuring the credibility and depth of accident causation analysis, accident report data from global civil aviation accidents in the year 2000 were collected as samples and subjected to structured processing. The accident data were categorized based on stages, types, and other fields, and statistical analysis was conducted. Utilizing the K-means algorithm, civil aviation safety risk factors were clustered from multiple perspectives, comparing the differences between structured and unstructured processing results. Various risk factors' combined impact on accidents was comprehensively considered. Key risky factors from the word cloud diagram and latent topic distribution in text data identified through LDA (latent dirichlet allocation) topic modeling were used to analyze accident causation. This study provides valuable reference information and safety recommendations for future civil aviation safety from multiple aspects.