A new palm vein classification method that combines a deep neural network and a random forest is proposed. First, the proposed method extracts the palm vein features using AlexNet, a pre-trained deep neural network model. Then, the principal component analysis is used to reduce the dimensionality of the extracted high-dimensional palm vein features in order to conserve storage space and reduce classification errors. Finally, the random forest is used for classification owing to its high tolerance to noise. Based on the PolyU, CASIA, and self-built databases, the test accuracies obtained are 100%, 97.00%, and 99.50%, respectively. Compared with the traditional methods, the proposed method overcomes the limitations of the manual feature extraction algorithms, effectively reduces the palm vein classification errors, and demonstrates better robustness.