Aiming at the current inaccurate predictions in 3D human pose due to factors such as occlusion and complexity of poses, this paper proposes an improved 3D human pose estimation algorithm to obtain accurate 3D human pose and enhance the performance of human pose estimation. Meanwhile, it adopts the graph attention block from the spatio-temporal graph attention convolutional network to construct the entire network. On this basis, the network structure of the global multi-head graph attention part is improved to facilitate better information propagation and fusion among nodes and capture semantic information not explicitly represented in the graph. Kinematic constraints are introduced as well, and a bone length loss is added based on the MPJPE loss. By the modeling of local and global spatial node information, the learning of kinematic constraints of human skeletal movements is achieved, including local kinematic connections, symmetry, and global poses. Empirical results show that the improved model effectively enhances the performance of human pose estimation. Compared to the original model on the Human3.6M dataset, a 1.8% improvement in mean per joint position error (MPJPE) and a 1.3% improvement in the Procrustes aligned MPJPE (P-MPJPE) after rigid alignment of predicted and true joints have been realized.