The diagnosis of H-type hypertension requires the determination of the patient’s plasma homocysteine content， which is inefficient and has a wound. Chinese pulse diagnosis helps doctors diagnose H-type hypertension by analyzing patient’s pulse activity and combining inquiry information. Therefore， we put forward a pulse-taking diagnosis classifiction model based on hybrid deep learning model， which can extract the local features via convolutional neural network（CNN） block， and long-term dependency features via Bi-directional long short-term memory（BiLSTM） block. The data come from 325 suspected cases of pulse diagnosis collected by Longhua Hospital affiliated to Shanghai University of Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We compare the proposed model with other machine learning models on the pulse diagnosis data respectively. The sensitivity， specificity， accuracy， F₁-score， receiver operating characteristic（ROC）area under curve （AUC） values of the proposed model are 79.71%， 69.56%， 77.17%， 83.96%， 0.850 0， respectively， higher than the performance of other machine learning models. The results show that our model has good performance and has good reference value for the clinical diagnosis of traditional Chinese medicine.