Text Clustering and Economic Analysis of Free Trade Zone Governance Strategies Based on Random Matrix and Subject Analysis

Abstract

The steps of generating basic data by the LDA model and calculating text by the weighted algorithm have a good effect on text clustering. In this paper, the LDA topic model is used to effectively improve the accuracy of strategy text clustering. FTZ economics text clustering simulates FTA economics text data and economic data, imports economics and economic figures and word lists, and uses the traditional vector space model for factor representation. After that, the text vectors are independent of each other, ignoring the semantic relationship, which affects the clustering analysis results. A Chinese text clustering algorithm based on semantic clustering is proposed. Based on the principle of cooccurrence and semantic relevance of words, the algorithm uses the collocation vector of feature words to construct semantic clustering; find the document vector with embedded semantic information. Finally, document vectors with embedded semantic information are used. Finally, K vector is used for cluster analysis. The simulation analysis in this paper shows that the economic growth of the free trade zone is the largest under the economics guidance, which can reach 15%.