This article studies the three-dimensional (3D) image analysis and sports training methods of sports technical characteristics. This research uses the current sports technology diagnostic 3D video analysis system as a platform to build a database and knowledge base based on athletes’ 3D sports information and sports parameters and uses algorithms based on artificial intelligence machine-learning machines to analyze sports data, learn from it, and learn from sports technology. Actions make analytical decisions and predictions. Then, it analyzes the human-motion behavior with the concept of traditional and virtual reality technology. The effectiveness of athletes’ technical movements, using mathematical statistics, artificial intelligence, and other research methods, integrates and draws on the research methods of sports biomechanics, graphical imaging, human anatomy, expert systems, and neural networks. A neural network not only inherits certain characteristics of biology but also has its own unique characteristics, such as large-scale parallel processing, strong fault tolerance, and self-learning functions. Neural networks have a wide range of applications in information processing, pattern recognition, optimization, and other issues. By analyzing the application status of artificial intelligence technology in sports, the development prospects of sports training based on artificial intelligence can be inferred. Based on the acquisition of sports-related data, the evaluation of functional action modes, sports techniques, etc., is established. The multi-target feedback training method ultimately helps athletes improve their training level. Experimental data show that for the human body walking toward the camera, the rotation angle between adjacent frames is close to 0°, and the translational position is basically 5 cm. The experimental results show that 3D image analysis and related sports training methods based on specific sports technical characteristics are conducive to athletes’ performance improvement.