濫觴於八零年代中期的財報分析決策支援系統，主要以專家系統為代表，其具有提升專家決策一致性與財務報表分析效率，該類系統的知識表達、決策解釋與運作機制多以法則式推理為主，然此一運作模式存在著過於僵化、缺乏彈性與自我學習能力以及所產生的結果難以涵蓋所有情況等缺失，因此亟需導入智慧型決策支援方案以提升決策的效能。為此，本論文針對知識擷取與知識表達兩項核心議題加以改良。有別於過去的相關研究，為了精確擷取出專業且內隱程度較高的領域知識以及考量財報分析知識本身，在知識擷取工作部份，我們整合了階梯方法、大聲思考法以及口語草稿分析法等技術；而在知識表達部份，為了顯示知識類別間的關聯性以及更明確地描述領域知識本身，本研究採用框架式以及本體論知識表達法的思維與特性來取代傳統法則式的知識表達法。為了增加系統運作彈性，知識框架法則部份採模糊邏輯表達式，以有效處理決策環境中之不確定性。再者，本研究同時考量知識庫完整性，以資訊擷取技術將財務報表中，對與專有名詞以及專業術語相似的字詞加以搜尋、比對並置入於知識庫中作為知識庫擴充之機制。本研究蒐集並分析民國93年度我國上市公司之實際資料並從台灣經濟新報資料庫取得實際個案之財務比率進行財務體質評估，其結果再與專家實際評估加以比較，以了解系統評估與專家評估是否一致，並由領域專家針對驗證結果進行評論，進而提升本論文所提出之智慧型決策支援系統的效能。再與框架式財報分析專家系統法則以及知識庫結構部份加以比較，進而取得本研究導入模糊邏輯以及本體論所帶來效益。

In view of the domain in financial and accounting expert system stands for financial statement analysis intelligent decision support system in 1980-mid. Knowledge representation, decision explanation and inference mechanism are used to rule-based approach have the disadvantages of lacking flexibility, self-learning and domain knowledge presenting. Our research ameliorates some issues as follows: 1. In knowledge elicitation, we combine laddering, thinking aloud and verbal protocol analysis to elicit tacit and professional knowledge. 2. To expend frame-based knowledge representation with ontology, we establish relations among knowledge classes and represent constitutive knowledge accurately. 3. To improve system flexibility and self-learning, we introduce fuzzy logic and information retrieval technique that process uncertainty decision and retrieve similar terms in this domain availably. For discussing our research performance, we collect and analyze the financial health data of listed from companies listed in Taiwan Economic Journal Data Bank in 2004 in order to verify consistence between results of system inferences and domain experts’ cognition, furthermore improve intelligent support performance, followed by comparing our research with frame-based financial statement analysis expert system in the quantity of rules and knowledge base framework to ensure enhance system efficiency. In the final step, to reduce system operation, development and maintenance complexity by verifying advantages of inducing fuzzy logic and ontology in our research.

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