Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsupervised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighborhood mutual information is proposed, and the feature clusters with certain event correlation are obtained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l₁ norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding algorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery.

文本事件挖掘作为文本挖掘处理中不可或缺的方法，引起了研究者的广泛关注。本文提出了一种基于邻域互信息和稀疏表示的事件知识图谱建模方法，即UKGE-MS。具体来说，UKGE-MS可以提高现有文本挖掘技术对高维未标记信息的理解和发现能力，解决传统无监督特征选择方法只注重从全局角度选择特征而忽略局部影响的问题样品的连接。首先，考虑样本局部信息对特征相关性评价的影响，提出了一种基于平均邻域互信息的特征聚类算法，得到具有一定事件相关性的特征簇；其次，结合局部线性嵌入算法的降维优势和稀疏表示的特征选择能力，设计了一种基于多维统计数据高阶相关性的无监督特征选择方法，以增强算法的泛化能力。选定的功能项。最后，通过稀疏表示构建事件知识图谱，从而增强所选特征项的泛化能力。最后，通过稀疏表示构建事件知识图谱，从而增强所选特征项的泛化能力。最后，通过稀疏表示构建事件知识图谱，l ₁范数。在五个真实数据集和合成数据集上进行了广泛的实验，并将 UKGE-MS 与五个相应的算法进行了比较。实验结果表明，UKGE-MS在事件聚类和特征选择方面优于传统方法，在文本事件识别和发现方面优于其他方法。