Objective To propose a learning model based on least square support vector machine (LSSVM) algorithm to improve the accuracy and efficiency for predicting clinical blood pressure data of traditional Chinese medicine (TCM). Methods The LSSVM learning model was used to predict the clinical blood pressure of TCM. By replacing the inequality constraints of support vector machine with LSSVM equality constraints, the quadratic programming problem was transformed into a linear equation solution problem to reduce computational complexity and speed up algorithm convergence. The clinical pulse diagram parameters and blood pressure data of 320 patients were collected. Three hundred of them were used as training samples, the remaining 20 samples were used as test data. The LSSVM learning model was used to predict blood pressure data according to the pulse diagram parameters of the patients. Results Experimental results showed that the LSSVM learning model had high prediction accuracy for blood pressure data. The LSSVM learning model based on polynomial kernel function had better learning and prediction abilities than the LSSVM learning model based on radial basis kernel function. The mean prediction errors of systolic blood pressure, diastolic blood pressure and mean arterial pressure obtained by the LSSVM learning model based on polynomial kernel function were 7.88%, 8.40% and 6.67%, respectively, which were lower than those obtained by the LSSVM learning model based on radial basis kernel function (7.95%, 9.70% and 7.48%, respectively). Conclusion The LSSVM learning model proposed in this experiment can be used to predict the blood pressure data of patients only by the clinical pulse diagram parameters, and is a good reference for clinical diagnosis of TCM.