Focused on the head pose flipping and the implicit spatial cues missing between image features when reconstructing human body from monocular images， a three-dimensional human reconstruction model based on High-Resolution Net （HRNet） and Graph Convolutional Network （GCN） was proposed. Firstly， the rich human feature information was extracted from the original image by using HRNet and residual blocks as the backbone network. Then， the accurate spatial feature representation was obtained by using GCN to capture the implicit spatial cues. Finally， the parameters of Skinned Multi-Person Linear model （SMPL） were predicted by using the features， thereby obtaining more accurate reconstruction results. At the same time， to effectively solve the problem of human head pose flipping， the joint points of SMPL were redefined and the definition of the head joint points were added on the basis of the original joints. Experimental results show that this model can exactly reconstruct the three-dimensional human body. The reconstruction accuracy of this model on the 2D dataset LSP reaches 92.41%， and the joint error and reconstruction error of the model are greatly reduced on the 3D dataset MPI-INF-3DHP with the average of only 97.73 mm and 64.63 mm respectively， verifying the effectiveness of the proposed model in the field of human reconstruction.