隨著資訊科技的崛起與網路快速的發展，紙本書籍轉換成電子書的形式漸漸受到矚目，人們可直接透過網路找到自己想看的書並且下載到電子書閱讀器，此乃大大提昇閱讀書籍吸收知識的便利性，然而，目前電子書已累積相當龐大的數量，因此在為電子書分門別類時，往往需要耗費大量的人力與時間。
而傳統分類技術，如決策樹(Decision Tree)、k個最近鄰居法(k-Nearest Neighbor, kNN)、貝氏分類法(Naïve Bayes)以及支援向量機(Support Vector Machines, SVM)等方法，其分類方法的過程中，通常會對文章擷取出具代表性的特徵字詞，形成特徵空間(Feature Space)。當文章越長時，則越容易產生大量的特徵字詞，造成空間維度過高，因此導致後續的分類處理很複雜，故在分類過程中會透過特徵選取的步驟，過濾不良的特徵字。而電子書內容長度通常是一般文章的十幾倍，若將傳統的特徵選取方式套用在電子書時，容易產生大量的特徵字詞而增加後續分類的複雜度，而且也會讓重要特徵字詞因為只集中在一些區塊而降低其整體權重，造成該字詞在篩選過程中被剔除。
因此，本研究在對電子全文進行特徵擷取時，將電子全文的文章結構納入考慮，利用文件分段技術，將全文分成數個較小長度的段落區塊，個別處理，分析字詞在各段落中的集中程度，藉此找出各段相對重要的特徵字詞，並期望利用所選取出的特徵字詞能夠提升分類的準確度。

With the exponential growth of information technology and Internet, paper books can be transformed into e-books. People can get these e-books form Internet and download them by e-reader. It enhances the convenience to absorb knowledge from books. However, the number of e-books has been very large. It costs lot time and energy to classify these e-books.
The traditional classification approaches like decision tree, k-nearest neighbor, naïve bayes, support vector machines, usually select the feature words from content. These words will form a feature space. The longer the article is, the more likely generate a lot of feature words, and the dimension of feature space is higher. It causes the follow-up of the classification process complicated. Therefore, the classification process steps filter undesirable feature words through feature selection. However, the length of e-books is usually much longer than the general article. With traditional approaches, e-books generate a large number of feature words, and cause the follow-up of the classification process complicated, and even lost important feature words because of the long length content reducing these words’ overall weights.
Therefore, we present a novel feature selection approach which applies a text segmentation algorithm. With this algorithm, e-books can be cut several segments.We analyze all words’ importance in these segments and select the inportant feature words for every segments. We expect that the feature words which selected by our approach can improve the accuracy of classification.

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