In the early methods of single-body pose estimation, the body parts were detected by using the artificially extracted Histogram of Oriented Gradient (HOG) features, and the human pose structure was represented by using the probability model map (rather than the tree model) [18]. After [PERSON] et al. [19] proposed DeepPose using AlexNet to regress the coordinates of human node locations to proceed, deep neural network-based models rapidly occupied the field of pose estimation. [PERSON] et al. [20] learned human pose structure by combining depth features and graphical models. [PERSON] et al. [21] proposed an iterative error feedback method (Iterative Error Feedback, IEF) to train Convolution Neural Networks (CNNs), which will repeatedly feed the input into the network and self-correct the network based on the prediction feedback. In recent years, the application of single-body pose estimation to athletes is mainly in the analysis of athletes' movements during competition. [PERSON] et al. [22] proposed a yoga training system by integrating computer vision techniques, and the system analyzes the trainer's pose by extracting body contours, skeleton, dominant axes, and feature points from the front and side views [23]. Then, based on the domain knowledge of yoga training, visualized posture correction instructions were proposed with high accuracy in correcting the trainer's posture. [PERSON] et al. [24] used a deep key frame extraction method for analyzing weightlifting sports training videos in order to monitor and analyze athletic poses of athletes training in professional sports, and the proposed DKFE outperformed the comparison methods in terms of key pose probability estimation and key pose extraction [25]. Pose estimation has become a popular research topic in sports movement analysis. In this stage, researchers are using single static images as input to integrate similar images in neighboring pixel points and complete the local search of human parts through superpixels [26]. They apply the variability part model to achieve human part recognition, which reduces the interference of background on part recognition. Additionally, the supervised training convolutional network provided by the ordinal depth based on single human joints can effectively improve the accuracy of remote mobilization detection in the scene. This paper specifically addresses the following problems based mainly on graph neural networks and single target pose estimation algorithms. The contributions of this paper are as follows:  
1. The model combining graph neural network and HRNet can effectively improve the accuracy and efficiency of pose estimation tests. This can help accurately identify the limb movements of athletes in the game and provide an effective reference for athletes' movement analysis. 2. Based on the end-to-end deep network for personnel detection and deconvolution operation through multiple upsampling and HR-Net, the shallow layer in the fusion neural network for human location information recognition improves single target recognition. This can circumvent some inspection errors caused by overlapping or shading and, to a certain extent, reduce the occurrence of missed and wrong inspections. 3. The graph neural network-based human pose estimation method is further optimized on the traditional graph structure-based method. This ensures adequate extraction of human pose features while reducing the time required for extraction and further improving the accuracy and efficiency of object detection and human pose estimation. This paper aims to investigate the action recognition of athletes in sports competitions using the skeletal point dataset and graph neural network. The paper begins by introducing the current development of computer vision and highlighting the specific application of single-body pose estimation in sports. The purpose and significance of the study are also explained.