当前社会治安形式总体稳定，但是随着互联网等新兴技术的兴起，随之带来的热点事件也不断增多，在这种情况下，需要总结归纳众多不同类型的事件，提炼出各类案件的特征，为公安机关在处置类似案件时提供有针对性的方案。本文通过搜集近几年的公安机关查处的各类案件，采用K-Means算法与LDA主题提取算法将所有案件进行聚类并提取主题，当出现新的案件，可采用朴素贝叶斯算法将新案件进行分类，并运用该类中类似事件的处置方式处理。

The current social security situation remains generally stable. However, with the emergence of new technologies such as the Internet, there has been a continuous increase in trending incidents. In this context, it becomes imperative to systematically summarize various types of events and extract characteristic patterns of different cases, thereby providing targeted operational solutions for public security authorities when handling similar incidents. This study collects five years of case data from public security enforcement records and employs both K-Means clustering algorithm and LDA topic modeling to analyze and categorize all cases while identifying their thematic features. When new incidents emerge, a Naive Bayes classifier can be implemented to categorize the case into existing clusters, enabling law enforcement agencies to apply established response protocols from similar historical cases. This methodology facilitates data-driven decision-making through systematic pattern recognition and predictive classification.