Objective To propose a frequency-domain pulse wave prediction model based on fusion attention mechanism, improving the low classification accuracy and poor generalization performance of hypertension time-domain pulse wave classification based on artificial intelligence technology. Methods Firstly, the time-domain pulse wave was transformed into frequency-domain Mel-scale frequency cepstral coefficient features to enhance its discriminability. Then, temporal convolutional network and Transformer structures were employed to extract the deep features of pulse waves, and self-attention mechanism and selective kernel attention were combined for decision fusion to extract relevant features. Floodings regularization method was used to indirectly control the training loss and prevent overfitting. A 5-fold cross-verification experiment was conducted based on 527 clinical pulse diagnosis data provided by Longhua Hospital, Shanghai University of Traditional Chinese Medicine and Shanghai Traditional Chinese Medicine-Integrated Hospital. Additionally, the extreme gradient boosting algorithm was employed to calculate the contribution rate ranking of frequency-domain pulse wave features, and the key factors affecting the classification accuracy of the model were analyzed to provide reference for the clinical auxiliary diagnosis of traditional Chinese medicine. Results The evaluation metrics accuracy, F1 score, precision, recall rate and area under curve value of the model proposed in this study were 0.939 6, 0.924 9, 0.940 9, 0.929 5, and 0.993 4, respectively. The static characteristics of the pulse wave, the contribution rate of the first-order difference and the second-order difference coefficients were relatively balanced, indicating that the degree of hypertension risk was not only related to the static characteristics of the pulse wave, but also to the dynamic characteristics of the pulse wave. Conclusion The proposed model has higher classification accuracy and generalization performance compared to typical pulse wave classification models.