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In recent years, there have been extensive studies and rapid progresses in automatic text classification, which is one of the hotspots and key techniques in the information retrieval and data mining field. Feature extraction and classification algorithm are the crucial technologies for this problem. This paper firstly proposed feature extraction algorithm based on key words, the algorithm selected key words set from special part of scientific papers, and employed mutual information to extract features. And then, proposed an improved hierarchical classification method, and realized hierarchical classification of Chinese scientific papers.

Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.