A Keyword Extraction Method for Chinese Text of Scientific Research Papers Based on Semantic Features and TextRank Algorithm
ZHANG Shichao
WANG Jianbin
MENG Hao
Abstract：To accurately extract and arrange keywords from the Chinese text of scientific research papers,a keyword extraction method for Chinese text of scientific research papers based on semantic features and the TextRank algorithm was studied.A semantic feature-based method for selecting candidate keywords from Chinese text of scientific research papers was used.In the Word2Vec tool,the Chinese text was converted into a word vector as the semantic features of the Chinese text of the paper.The semantic features were input into convolutional neural networks,and the semantic features belonging to candidate keyword types were extracted through classification.The text words they belong to were used as candidate keywords.By using the TextRank algorithm-based keyword extraction method for Chinese text of scientific research papers,a graph model for extracting keywords was constructed by using the average information entropy,part of speech,and position of the candidate keywords as the keyword extraction indicators.The comprehensive weights of the candidate keywords were calculated,and the candidate keywords were arranged in descending order.The top-ranked candidate keywords were used as the final extracted keywords to complete keyword extraction from Chinese text of scientific research papers.The tests show that this method can improve the accuracy of keyword extraction and keyword ranking in Chinese text of scientific research papers.
Keywords：Semantic featureTextRank algorithmScientific research paperChinese textKeyword extractionConvolutional neural network
Publication Date：2025-09-30
Online Publishing Date：2025-10-10(First online date of this platform, not the publication date of the document)
Pages：7( 188-194 )