Blood pressure is an important physiological parameter of the human body, which can reflect the pumping function of the heart, peripheral vascular resistance, and blood volume. Non-contact continuous measurement of blood pressure is of great significance in daily life and many applications. This paper obtains the relevant pulse wave signal from the facial video, and then extracts the characteristic parameters of the signal that are highly correlated with blood pressure, so as to use these parameters to establish a neural network model for blood pressure estimation and optimize it by genetic algorithm. Through verification, it is concluded that the genetic algorithm optimized BP neural network ( GA-BP) model estimation ability and fitting accuracy are significantly improved, and the estimation results meet the blood pressure measurement standards while realize the non-contact continuous estimation of blood pressure. The estimated accuracy rate of systolic blood pressure was 93. 1%, and the estimated accuracy rate of diastolic blood pressure was 96. 6%. Therefore, the establishment of GA-BP model by pulse wave characteristic parameters is an effective non-contact estimation method of blood pressure.