The main disadvantage of currentmethods ofdynamic heart rate measurement is the low accuracy. In order to improve the problem, deep learning algorithm was introduced to extract the photoplethysmograph(PPG) of heart rate value. In this paper, the pulse signals of 15 healthy subjects participated in the experiment was acquired under the different velocityas the input of stacked autoencoders network (SAE). At the same time, electrocardiograph(ECG) signal as the label of that network was gathered by a standard ECG collector whichhas high antiinterference. Combining with the deep learning algorithm, SAE was trained,in which the pulse signal with strong interference was fitted to thesignalof sinelike wave with the characteristic of accurate heart rate, in order to realize the extraction of heart rate under the condition of serious disturbance under sports conditions.The experimental results show that compared with the output signal of SAE, the proposed method obtains smaller error value of the heart rate (1.165 8 bpm), which showsthe effectiveness of heart rate measurementusing deep learning algorithm, and provides a new way for the sportiveheart ratemeasurement.