Text Clustering Algorithm Based on Semantic Graph Structure

As semantic information is often missing in text representation, this paper proposes semantic graph structure to represent text and optimize graph structure by semantic similarity matrix. Then calculate the similarity of semantic graph structure by using the maximum common sub-graph of graph theory. Finally, K-means algorithm will be applied to expand Chinese text clustering to improve text clustering effect. Experimental results show that the proposed algorithm based on semantic graph structure is more conducive for the representation of semantic information. It significantly improves the accuracy of text similarity in the text similarity measure and will be applied to text clustering to further improve the effect of text clustering by adjusting the corresponding parameters.