近年來，由於整體經濟環境的快速變遷，造成企業發生財務危機的可能性隨之逐年增加，因此，建立一個有效的企業危機診斷模式，是當前學術界與實務界相當重要的課題之一。本研究利用整合鑑別分析與類神經網路的兩階段建構模式方法，建構企業危機診斷分類模型。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，亦加入了智慧資本指標，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我的真實價值，並做出正確的決策。本研究經由理論與文獻的探討，建立了新的企業危機診斷模式，在經過實證的結果發現，經由鑑別分析方法針對所考量之衡量企業危機指標進行分析，得知企業經常失敗的原因，除了受到傳統財務構面指標的影響外亦受到智慧資本構面指標的影響。此外，有關整合鑑別分析與類神經網路方法所建構之企業危機診斷模式亦能確實有效的降低企業危機診斷的誤判情況，是以無論在學術研究或實務工作上，實有其相當之助益。

In these few years, the rapid change of global economic environment has increased the occurrence possibility of financial distress. Therefore, to build up an appropriate finanical distress diagnosis model has become a very important task in industry. The objective of the proposed study is to investigate the performance of enterprise distress diagnosis by integrating the artificial neural networks with discriminant analysis technique. In addition to the financial capital indicator, the intellectual capital (IC) indicator is also included in the model to measure the assets of companies. The results from the present study indicate that the proposed combined approach predict much accurate and converge much faster than that the conventional neural network approach. In the other words, a neural network takes a long time to achieve an accurate result without a good initial estimate from discriminant analysis approach. Moreover, we find out that the diagnostic correctness of enterprise distress is significantly influenced by both traditional financial indicators and IC indicatiors.