現今由於之前企業併購熱潮，使得企業到底價值多少？企業是否能夠還有前景？這些問題不僅僅是投資者所關心的問題，也同樣是會計師及企業評價者所關心的問題。又現今已邁入知識經濟時代，企業已從過去以土地、廠房、設備等固定資產來產生企業價值，轉而以服務、品牌、專利等無形資產為主要的企業價值時，企業的價值又要如何來估算。而這些問題都一再的顯示出“企業評價”的重要性。 在進行企業評價之前，企業評價模型中之資料項的取得更是關係著最後評價結果的好壞。在企業評價資料項中，可分為財務性及非財務性。財務性資料項由於定義清楚，所以在資料的收集上較非財務性資料容易。但我們發現過往之資料收集方式並不足以應用在企業評價非財務性資料項的收集上，且現行大多採用人工處理資料的方式，不僅耗費大量時間及成本，又因人工輸入而有資料輸入錯誤之風險，使得資料的正確性大幅降低。故本研究提出一自動化擷取年報中企業評價非財務性資料項之方法，希望藉此方法達到簡化資料收集過程，提高資料的正確性。 / Because of the trend of the business combination, now, more and more people concern about “how much value does a business have?” And “does the business still have any perspectives?” This not only get investors’’ interest, but also the accountant and business valuator. Now we already get into a new economy, called knowledge-based economy. When the businesses are not just use fixed asset, such as facility, factory and land to earn money, but also earn their money by providing services, making brand, or sell patents for live, how to measure the business’s real value and what the real value for the business is. These problems all shows that the importance of “Business Valuation.” Before calculate the business value, the most important thing is to collect the data or data category for business valuation. There are two kinds of business valuation data item. One is financial data item; the other is non-financial data item. Because of the financial data item’s clear definition, the data collection process of financial data item is easier than non-financial data item. And the data collection in the past is not fit for today, and now most valuators use manual way to process these data. This way not only wastes the time and money, but also lowers the correctness and raises the risk of mistype during the process of data collection. In this thesis, we propose an approach to automatic extract business valuation data category from annual report by using the technology of data extraction.