In order to quickly obtain the complete feature information of the human pulse signal, and quickly and accurately identify the correlation between the pulse feature information and the human disease.The study uses the time-domain characteristics of multi-period pulses and the Hilbert-Huang-Transform(HHT) based on Ensemble Empirical Mode Decomposition（EEMD） to obtain the instantaneous frequency and amplitude as the frequency-domain characteristics. The time domain and frequency domain features as input fused convolutional neural network to identify and classify the pulse characteristics of the human body.The pulse signals of three clinical symptoms were obtained from the MIT-BIH standard database for experimental analysis. Finally, through experiments, the accuracy of pulse feature recognition and classification is 91.88%. Using EEMD-based HHT as a supplement to time-domain features, time-frequency feature mixing can make the PPG pulse signal complete characterization, and perform classification experiments on the convolutional neural network to obtain better classification results. Methods willing clinical diagnosis of intelligent development, improve the accuracy and efficiency of clinical diagnosis to provide a good role in promoting.