目前教育部正積極推展「學生體適能護照全面辦理」計畫，為因應學校體適能發展之需求，筆者嘗試應用倒傳遞類神經網路，建立運動強度與運動持續時間的運動處方的診斷模式。研究方法：本研究是以90位19-22歲的大專學生為受測對象，採集年齡、心肺耐力與身體質量指數三項體適能檢測資料，運用倒傳遞類神經網路輸入層、隱藏層、輸出層三層的網路架構，以C++程式語言，編寫成電腦程式來進行演算，進行網路的學習及回想演算，推論出受測者運動強度與運動持續時間的運動處方。結論：經由模擬預測發現，倒傳遞神經網路運用在運動處方的診斷上是可行的，此技術亦值得相關的研發單位史進一步開發成電腦軟體應用於實際的體適能推測上。

Recently the Ministry of Education is actively promoting the overall implementation of passport on students' physical fitness. To meet the needs of the schools targeting developing students' physical fitness, this study attempted to apply back-propagation neural network to establish a diagnosis model of exercise strength and duration. This study took ninety college students as subjects who were aged from 19 to 22 years. Data of the participants' age, aerobic fitness、and body composition were collected. Then the three-layer structure in the back­propagation neural network-the input layer, the hidden layer, and the output layer-was programmed in C++ programming language to compute the network learning and recall processes and thus infer a diagnosis model of the exercise strength and duration fitness of the subjects. The finding of this study showed that after the computation of the network simulation, the back-propagation neural network was proven to be capable of providing a useful diagnosis model. Therefore, we consider it to be worthwhile for the appropriate authorities to develop computer programs for testing physical fitness.