To solve the problems of consistency and efficiency in manual classification,a multi-layered automatic book classification is applied to library cataloging work,and semantic concepts are introduced as a strategy to improve the classification effect,so as to improve the classification quality.In order to solve the problem of insufficient data and literature features,the proposed strategy uses Word2Vec,which can extract the deep semantic relationships between words and contexts,to expand words features for improving the classification performance.With the collection of data from Cxstar Ebook,Naive Bayes,SVM,Decision Tree C4.5,and KNN are applied to the multi-layered automatic book classification.Regarding the proposed semantic-based approach,this study uses Word2Vec as a tool for training corpus.First,a thesaurus is built by the training results,and next the word features of the data set for classification are expanded.Finally,the classification effect is evaluated based on the accuracy level.Experimental results show that the performance of the multi-layered automatic book classification outperformed the traditional automatic book classification in a library environment.The proposed strategy can indeed improve the accuracy of book classification.
classification numbermulti-layeredautomatic book classificationWord2Vec
万方数据
维普
