數位化文件已成為人們取得、儲存以及傳播知識最重要的形式之一，並且隨著知識管理逐漸受到重視，數位化文件的數量亦呈現急遽的成長。因此，如何有效的管理龐大的文件集便成為一個相當重要的議題。
文件分類乃是根據文件的內容給予一適當的類別，使其方便組織、管理以及利用，因此有不少學者提出了自動化文件分類的方法。而縱觀現有的技術，幾乎都是將所有的特徵詞視為互相獨立，因此可能會因為忽略了特徵詞與特徵詞之間或特徵詞與多個文件之間潛在隱含的關係而導致分類效能不彰。此外，資訊的不完整亦可能使得文件無法被正確的分類。
根據上述的問題，本研究提出基於概念延伸之文件分類法來進行改善。主要是採用正規概念分析來進行文件的類別推論，並加入擴展查詢的概念以避免因資訊不足而造成分類錯誤。根據實驗的結果，證實本研究方法在分類的效能上的確優於以往的分類方法，並且應用擴展查詢於文件分類領域中也能有效的提升分類的正確率。

Digital files have become one of the most important forms which people used for acquisition, storage and dissemination knowledge. And with the knowledge management has been gradually emphasized, the number of digital files also has been dramatic increase. As a result, how to manage a large set of files effectively becomes a considerable issue.
Text classification is a technique that assigns appropriate categories to a document based on its content, making it easy to organize, manage and use. Therefore, there are many scholars proposed methods of automated text classification. But examining the present technology comprehensively, almost all of them assume the feature words in documents are independent of each other that may causes error classification because of ignoring the implied relationship between words and words or words and multiple documents. What more, the incomplete information of unknown document also may make it couldn’t be classified correctly.
According to the above problems, this study proposes an extended classification method based on concept expansion to improve the present efficiency. It mainly use the Formal Concept Analysis to infer the categories of unknown document, and involve the concept of query expansion in order to avoid classification errors caused by lack of information. The experimental results confirmed the effectiveness of proposed method in this study which is obvious better than that of previous classification methods, and applied the query expansion in the field of text classification can effectively improve the classification accuracy.

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