In image classification, convolution neural network has made great progress in face recognition. When convolution is used to extract face image feature information, when the number of convolution kernels is limited, the feature values, such as hair, texture, may not represent the main features of the person well, resulting in the reduction of recognition rate. To solve this problem, a face recognition method based on feature information convolution neural network is proposed in this paper. In the process of image processing, ingular value decomposition is used to select the first four singular values to represent the main features of the face, and most of the useless feature information is quickly filtered out. The convolution network can improve the receptive field of the network without losing the information of the feature map, and fuse the most representative feature information. The convolutional neural network model and the structural model of feature fusion based on singular value decomposition are used to realize face recognition. The simulation results show that this method reduces the training time of the algorithm and improves the accuracy of face recognition.