关键词: 语义文本分类, 概念特征, 本体, 支持向量机

Abstract: Text classification is the key method of mass text messages organizing and processing.Most current text classification models use keywords eigenvector to describe the text resources which makes the vector high dimensional and sparse.This paper introduces the concept-features instead of keywords to describe the text resources，which improves the accuracy of text resources description.A concept-features-based semantic text classification model is put forward in this paper.The results of simulation experiments show that the model can overcome the vector space’s high-dimensionality and sparsity，and ensure the orthogonality of the vector space.The semantic text classification presents a fine performance both in efficiency and accuracy.

Key words: semantic text classification, concept-features, ontology, Support Vector Machine（SVM）