Fuzzy Rules Algorithm In The Classification Of Educational Information

The rapid development of Internet causes mass increment of network education information. However, because of the complexity of network structure and the irregular distribution of education information, the usage of these information always be limited. So, the problem that how to get and use these information more effectively raises people's concern. On the one hand, the classification of education information can provide convenience for retrieving and using education information resources. On the other hand, through the classification and analysis of relevant data, statistical description of current education resource can be formed, which could provide reference for education teaching staff, research workers and education managers.Through the research on domestic and foreign technologies, the dissertation designs a classification model for education information according to the research purpose of the issues. Take, for example, introduction of primary and secondary schools. Because of the fact that these kinds of data have less feature items and unequal weights, taking the improved TFIDF algorithm as the fundamental method to extract characteristics, we use the strategies of denoising, processing synonym features based on fuzzy set to build category vocabularies, and then we classify data using the classification model which is based on category vocabularies and fuzzy rules. Around the fuzzy rule, classification model consists of three core links:the establishment of category vocabulary and fuzzy set, text analysis and realization of classification algorithm.In order to validate the overall performance of classification model, the comparison experiment was made between fuzzy rule algorithm and other related algorithm. The experimental result shows that as for the classification of education information, the classification model based on fuzzy rules has better performance than other algorithm on speed and accuracy. This conclusion is also applicable to the classification of text which have less feature items and uneven distribution of weight.