近年來，由於企業環境經營巨變，造成整體經濟所面臨的狀況更加艱鉅，而企業財務危機發生的可能性亦隨之提升。因此，建立一個有效的財務危機診斷模式，是當前學術界與實務界的一個重要課題，本研究整合資料探勘與資料包絡分析建構模式方法，建構企業危機診斷分類能力。此外，在探討企業危機的衡量指標上，本研究除了參考一般傳統財務性指標外，亦加入了經營績效指標，希望能藉由更完整多元的企業資訊，來幫助企業本身評估其自身的真實價值，並做出正確的決策。本研究經由約略集合理論針對所考量之衡量企業財務危機指標進行分析，得知企業發生財務危機的原因，除了受到傳統財務構面指標的影響外亦受到經營績效指標的影響。此外，整合資料探勘技術與經營績效指標所建構之企業財務危機診斷模式亦能確實有效降低企業財務危機診斷誤判的情況，是以無論在學術研究或實際工作上，實有其相當的助益。

Over the last few years, rapid changes in the global economic environment have increased the possibility of financial failures occurring. Therefore, constructing an appropriate financial distress diagnosis model has become a crucial task for the industry. The objective of this study is to investigate enterprise financial distress by integrating data mining with a data envelopment analysis (DEA) indicator. In addition to a financial indicator, the DEA indicator is also included in the model.
The results indicate that the combined approach proposed in this study enables greater prediction accuracy and convergence speed compared to that of conventional data mining. Additionally, we discovered that the accurate diagnosis of enterprise financial distress is significantly influenced by
both traditional financial indicators and the DEA indicator.