Aiming at upgrading the performance of face pose classification, we proposed an algorithm of face pose classification based on deep learning and gradient information fusion. First, the pixel gray intensity features and the features of gray intensity difference nearby each pixel from a face image are extracted. Then, these features of face images are processed with deep learning technique through a dedicated three-layer restricted Boltzmann machines network, which has been trained by a large number of samples. Finally, a corresponding relation between fusion deep learning features and the labels of face pose classifications is built through a Softmax classifier. The experiment results show that the proposed algorithm achieves a state of the art classification accuracy, generally higher than 95%, when learning and testing on CAS-PEAL-R1 face database