As a new generation of high-precision biometric recognition technology, palm vein pattern recognition technology is widely used in the field of personal identification. However, its recognition effect is limited by the quality of the image. Low-quality images often result in low recognition accuracy. How to effectively evaluate the image quality and screen out high-quality images becomes an important research issue in palm vein recognition technology. This study aims to solve this problem and proposes a multi-parameter palm vein image quality evaluation method based on BP-AdaBoost neural network. According to the quality characteristics of the palm vein image, the evaluation indexes (contrast, entropy, sharpness and equivalent visual number (enl)) of multiple parameters are proposed. Based on the excellent nonlinear fitting characteristics of BP network, multiple evaluation parameters are used as network input, the classification result is network output, and 10 BP weak classifiers are trained. On this basis, the final strong classifier is obtained by AdaBoost algorithm. The experimental results show that compared with the traditional weighted fusion evaluation classification method, the classification results have higher accuracy and the system has good application value.