A Recognition Method of Measuring Literature Topic Evolution Paths Based on K-means-NMF

Abstract

In this study, we propose a recognition method of measuring literature topic evolution paths based on K-means-NMF in order to address problems such as the unobvious effect of topic clustering, high degree of mixing in clustering results, and unclear topic evolution paths that exist in the current research of topic evolution analysis. Firstly, we enhance the traditional NMF (Nonnegative Matrix Factorization) topic model by combining the K-means clustering algorithm with the NMF model to improve the accuracy of topic clustering and reduce the correlation among topics. Secondly, we perform the topic co-occurrence analysis based on the clustering results to identify important topic categories for recognizing critical evolution paths to solve the problem of multiple possible evolution paths in the experiment. Thirdly, we adopt the Word2Vec model to calculate topic word vectors in a semantic context to improve the accuracy of the correlation strength between topics at adjacent stages. Finally, we adopt the above method to conduct an empirical study using intelligent algorithms as an example. The experimental results show that this research method effectively identifies important topics and topic developments in the subject area, which can support scientific research and science and technology policy development.