本文針對高雄市失業率，利用倒傳遞類神經網路模式，進行預測並加以討論。其 中，對網路模式之相關參數，例如：學習率及隱藏層之神經元數目等，皆有進一步之探討。 文中所使用之資料數據為，自民國 72 年 6 月至民國 81 年 2 月之高雄市月失業率資料。 除類神經網路模式之應用外，時空數列分析，單變量時間序列模式及狀態空間分析模式等方 法，亦被引用來評估預測之好壞，根據結果顯示，類神經網路模式能提供高於其它方法，約 40% 之預測準確度。

　　　　　A backpropagation neural network was designed for short term unemployment rate forecasting. The forecasting was performed on Kaohsiung unemployemtn rate to demonstrate the predictive capability of the network. Extensive studies were performed on the effects of various factors such as learning rate and the number of hidden nodes. The monthly unemployment rate from June 1983 to Feb. 1992 was evaluated by neural network model and alternative methods, such as space-time series analysis, univerariate ARMA model and state space model, the utilization of neural model significantly provide better forecasting of unemployment rate than any other alternative methods. Generally, the prediction precision of the neural network is 40% higher than the prediciton made by the other models.