At present, since the method of measuring blood pressure with cuff cannot work in a high-pressure environment, in order to solve the problem of measuring blood pressure in a high-pressure environment, we propose a K-nearest neighbor continuous non-invasive blood pressure prediction model combining pulse wave conduction time and heart rate variability. In this study, rabbits were used for the experiment. They were pressurized from normal pressure to a depth of 1 000 m in an animal hyperbaric chamber. During this process, the electrocardiogram, pulse wave and invasive blood pressure of the rabbits were collected. Taking a duration of 30 s as one data point, and finally using data with a depth of 0~300 m for training and data with a depth of 300~1 000 m for prediction, the results of the mean absolute error±standard deviation of the K-nearest neighbor model in predicting the systolic and diastolic blood pressures of rabbit 1 were 2.2± 1.5 mmHg and 1.9±1.4 mmHg, respectively. The results of systolic and diastolic blood pressure for rabbit 2 were 1.7±1.3 mmHg and 1.7±1.5 mmHg, respectively. The results show that the method proposed in this paper has achieved good results in predicting blood pressure for different individuals in a high-pressure environment and provides ideas for blood pressure monitoring under high pressure.