Research on Enterprise Hidden Danger Association Rules Based on Text Analysis

Abstract

Descriptive fields are used in the daily operation of an enterprise as a dimension for recording hidden danger letters. However, it is quite difficult to extract corresponding effective data from such text information to guide the operation of the enterprise. In view of this problem, this paper first grouped the hidden danger data of different enterprise types, then used Chinese word segmentation technology to transform the corresponding descriptive fields into structural data, extracted the topic model using Latent Dirichlet Allocation (LDA) algorithm, and finally used Apriori algorithm to find the association rules of hidden dangers under different enterprise types. After the analysis of the experimental results, the association rules validated accord with common sense and can provide data support for hidden danger supervision.