本研究探討敏感度分析之技術是否能讀取神經網路所學得之知識，以及是否能用來評估神經網路之學習績效。本研究以選擇權定價公式（Black-Scholes formula）之模擬為研究對象。本研究之實驗結果顯示敏感度分析之技術能讀取神經網路所學得之知識，也能用來評估神經網路之學習績效。 This study presents the methodology of sensitivity analysis and explores whether it can be an alternative evaluation criterion as well as a tool to "read" artificial neural networks' knowledge. The simulation of the Black-Scholes formula is employed for this object. Since, in the Black-Scholes formula, the mapping relationship between the call price and five relevant variables is a mathematically close form, it is feasible to verify the validity of the methodology of sensitivity analysis. The experiment results are promising; they show that both values of the sensitivity analysis and the partial derivative of the Black-Scholes formula are consistent. Furthermore, the sensitivity analysis can be an alternative criterion for comparing the effectiveness of ANNs.