With the high-speed operation of society and the increasing development of modern science, people’s quality of life continues to improve. Contemporary people are increasingly concerned about their quality of life, pay attention to body management, and strengthen physical exercise. Volleyball is a sport that is loved by many people. Studying volleyball postures and recognizing and detecting them can provide theoretical guidance and suggestions for people. Besides, when it is applied to competitions, it can also help the judges to make fair and reasonable decisions. At present, pose recognition in ball sports is challenging in action complexity and research data. Meanwhile, the research also has an important application value. Therefore, this article studies human volleyball pose recognition by combining the analysis and summary of the existing human pose recognition studies based on joint point sequences and long short-term memory (LSTM). This article proposes a data preprocessing method based on the angle and relative distance feature enhancement and a ball-motion pose recognition model based on LSTM-Attention. The experimental results show that the data preprocessing method proposed here can further improve the accuracy of gesture recognition. For example, the joint point coordinate information of the coordinate system transformation significantly improves the recognition accuracy of the five ball-motion poses by at least 0.01. In addition, it is concluded that the LSTM-attention recognition model is not only scientific in structure design but also has considerable competitiveness in gesture recognition performance.