Human pose estimation is one of the basic tasks in computer vision， which can be applied to the fields such as action recognition， games， and animation production. The current designs of deep network model mostly use deepening the network to obtain better performance. As a result， the demand for computing resources is beyond the computing power of embedded devices and mobile devices， and the requirements of actual applications can not be met. In order to solve the problems， a new lightweight network model integrating Ghost module structure was proposed， that is， the Ghost module was used to replace the basic module in the original high-resolution network， thereby reducing the number of network parameters. In addition， a non-local high-resolution network was designed， that is， the non-local network module was fused in the 1/32 resolution stage of the network， so that the network has the ability to obtain global features， thereby improving the accuracy of human pose estimation， and reducing the network parameters while ensuring the accuracy of model. Experiments were carried out on the human pose estimation datasets such as Max Planck Institut Informatik （MPII） and the Common Objects in COntext （COCO）.Experimental results indicate that， compared with the original high-resolution network， the proposed network model has the accuracy of human pose estimation increased by 1.8 percentage points with the number of network parameters reduced by 40%.