隨著網際網路的快速發展、寬頻網路的日漸普及，網路上流傳的資訊越來越多樣化，新聞電子化己成趨勢。利用電子新聞收集資料及彙整資訊的人也越來越多。大量的電子資訊造成了網路資訊過載的問題。目前有許多的紙本的報紙都有提供電子新聞供人們利用網路快速瀏覽新聞資訊，也有部分新聞提供了電子資料庫知識查詢的功能，但多數只提供關鍵字查詢的方法，使用者仍需面對著大量的搜尋結果，因此文件分類在處理和組織大量的新聞資料中扮演著一個極為重要的角色。目前有許多研究使用統計及機器學習的方法進行新聞文件分類；也有研究針對新聞進行階層式分類，讓使用者更精確的減少所搜尋到的文件數量。不過目前對於新聞階層式分類的研究，在分類上都是從一層到分類的最後一層皆使用同一種分類方法。
　　為了幫助新聞資料收集者更快速且準確的分類新聞資料，本研究在各個階層使用不同的分類方法及資料內容將新聞文件做階層式的分類。針對電子新聞找出一種最佳分類方法的組合，以提高分類上的準確度。經由實驗後發現SVM還是較佳的分類方法，但在某些不同的評估情況下會有不同的組合。

Online news has became a trend, manual text classification distributes text documents into one or more pre-defined categories of similar documents. It is essential to develop an automatic classification method to reduce manual work. Currently, the news is a hierarchical structure. We wonder if a classification method applicable to each level.
　　In this paper, we first collected most popular classification methods. Several suitable combinations are selected and applied to different hierarchical level. Unlike other papers, they all apply one method in all level to classify news. We use three most popular text classification algorithms, Support Vector Machine, Naïve Bayes and K-Nearest-Neighbor, to classify Reuter Corpus, Volume 1. We expect to find a better combinatorial classification method to improve classification accuracy and performance.

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■ 網站資料
NIST, http://trec.nist.gov/data/reuters/reuters.html
SVMmulticlass, http://svmlight.joachims.org/svm_multiclass.html
Support Vector Machines, http://www.support-vector.net