Abstract:  There are more and more education resource of information in the period of big data on the Web. The classification requirement of a great number of education texts of being multi-class, multi-level can be satisfied by hierachical classification. Therefore, the class representation model of traditional hierachical classification has high-dimension and sparse problem, and it’s lack of semantic understanding. To solve the above problems, the classification method of education text based on hierachical class topic graph model was proposed. The text set was modelled by the hierachical class topic graph model. Probability matrices of hierachical class-word of the texts were obtained. In order to further improve correlation between feature words and classes, the combined feature was extracted by the complementarity of the three kinds of way extracting feature. Finally, the texts were classified by the hierachical SVM classifier. The analysis on simulation result indicates that the evaluation values of MacroP, MacroR and MacroF1 etc increase to some extend, comparing to traditional hierachical classification method. Therefore the method has good classification effect of Internet education text, and application prospect.

Key words: education resource, hierachical classification, text classification, topic graph model, probability matrix, support vector machine(SVM)