Abstract: As the complexity of the network, traditional Bayes classification algorithm cannot meet the demand of text filtering. Multi Word-Bayes (MWB) classification algorithm is proposed. On the one hand, Term Frequency-Inverse Document Frequency (TF-IDF) feature weight is introduced in MWB algorithm to optimize the traditional Bayes algorithm which only considers the problem of word frequency, but doesn’t consider the relationship between the texts. On the other hand, the new algorithm views the relationship between the word and the word as an important reference, which overcomes the traditional Bayes classification algorithm ignoring the semantic analysis on the classifier training. Experiment results show that MWB classification algorithm is of better classification effect on the text filtering.

Key words: Bayes classification algorithm, TF-IDF, semantic analysis, text filtering