It is difficult to extract suitable face feature for classification, and the face recognition accuracy is low under unconstrained condition. To solve the above problems, a new method based on deep neural network and weighted fusion of face features, namely DLWF, was proposed. First, facial feature points were located by using Active Shape Model (ASM), then different organs of face were sampled according to those facial feature points. The corresponding Deep Belief Network (DBN) was trained by the regional samples to get optimal network parameters. Finally, the similarity vector of different organs was obtained by using Softmax regression. The weighted fusion of multiple regions in the similarity vector method was used for face recognition. The recognition accuracy got to 97% and 88.76% respectively on the ORL and LFW face database; compared with the traditional recognition algorithm including Principal Components Analysis (PCA), Support Vector Machine (SVM), DBN, and Face Identity-Preserving (FIP) + Linear Discriminant Analysis (LDA), no matter under the constrained condition or the unconstrained condition, recognition rates were both improved. The experimental results show that the proposed algorithm has high efficiency in face recognition.