Photoplethysmography (PPG) signal, which contain abundant information related to blood pressure, can be used for cuffless blood pressure measurement. However, PPG signal is easily disturbed by noise, and the accuracy of blood pressure measurement depends on high quality characteristics of the PPG signal. Therefore, we propose a method integrating modern signal processing and pulse wave characteristic parameters analysis to improve the accuracy of cuffless blood pressure measurement based on PPG signal. Firstly, the effective PPG signal is reconstructed by combining ensemble empirical mode decomposition and signal quality detection algorithm to suppress noise interference, so as to ensure the validity of waveform and frequency characteristics of PPG signal. Combined these PPG characteristics and individual parameters, the BP neural network blood pressure measurement model is established. The method, called mean impact value, is used to select the parameters to reduce redundancy, then the genetic algorithm is used to optimize the neural network. Finally, we establish the final blood pressure measurement model. The experimental results show that the systolic and diastolic blood pressure measurement errors ≤ 10mmHg are 93.1% and 94.83%, respectively, by using the proposed method. The results meet the blood pressure measurement standards and can effectively realize the cuffless blood pressure measurement.