隨著網際網路的發達，由於電子郵件的傳遞具有經濟迅速的特點，越來越多使用者以電子郵件做為聯絡工具，垃圾郵件的氾濫成為收件者一大困擾，基於上述原因，本論文採用分類效果佳、速度快的Naïve Bayes演算法為垃圾郵件過濾器，並以相關性係數與距離係數權重計算建立重要關鍵詞相互關係的特徵擷取法與使用N-gram中文斷詞的郵件前置處理法來提升中文垃圾郵件分辨率。系統效能則使用TREC 2006中文郵件資料集與TREC 2007英文郵件資料集，以k-fold方式進行評估。實驗數據證明SP(Spam Precision)與SR(Spam Recall)整體而言都較其他研究成果為佳。

As the Internet developed, more and more people use e-mail as a communication means. At the same time, spam flooding has also become a serious problem for recipients. This paper chose Naïve Bayes Theory as the classifier in spam filter because of good classification results and classification speed. To reduce the influence of content tampering from spammers and to enhance the impact of spam on the resolution, we use correlation and distance coefficients addition with features to establish important keywords are related to each other. Another pre-processing for Chinese e-mail, we use the N-gram to do segment job. The datasets we use are the TREC 2006 data set of Chinese e-mail and TREC 2007 of English e-mail. Experiments show that our SP (Spam Precision) and SR (Spam Recall) Overall results are better than the other researches.

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