Aiming at the low accuracy of the large-pose face alignment algorithm, this paper designs and implements a new hierarchical parallel and multi-scale Inception-resnet network to achieve large-pose face alignment. Firstly, a four-class Hourglass network model is constructed. The model directly inputs images for face alignment in an end-to-end manner. Secondly, the network internally uses pre-set parameters for sampling and feature extraction. Finally, the corresponding face feature points are directly output. A two-dimensional coordinate point drawing of the image and the equivalent face size is extracted, and the proposed method is tested on the AFLW2000-3D data set. Experimental results show that the normalized average error of this method is 4.41% for any unconstrained two-dimensional face image. Compared with the traditional method, the positive face attitude image outputted in this paper has high visual quality and fidelity.