Face recognition is an important field of visual recognition. Because of the large scale of variations in face recognition, namely drastic changes in illumination and pose, occlusion problems, and complex image background, it is difficult to recognize the face under such unrestricted conditions. In order to solve these problems, a multi-Inception model based on Tensorflow platform is proposed in this study. By combining multiple Inception knots, a multi-Inception-V3 model based on Tensorflow platform is proposed. The structure is connected in series, which realizes the convolution and re-aggregation of multiple dimensions at the same time, and improves the accuracy of face recognition. The experimental results show that the proposed method can extract more discriminant face features with fewer parameters. Compared with the classification loss method and the fusion of other metric learning methods, it improves the accuracy of face recognition under unconstrained conditions.