# AI-Driven Smart Transformation in Physical Education: Current Trends and Future Research Directions
## 2 Literature Review
### Applications and Advantages of LDA Topic Modeling  
In recent years, with the development of big data technologies and text mining tools, researchers have increasingly adopted data-driven methods to analyze large-scale text data, aiming to reveal key themes and development trends within specific research fields. This data-driven approach has been widely applied in academia, especially in the natural sciences, technical sciences, and health sciences, where methods like keyword network analysis are used to understand the structure and evolution of scientific knowledge. This approach not only reveals research topics across different disciplines but also helps researchers identify potential research hotspots and future trends, enhancing the understanding of disciplinary development [13; 14]. However, keyword network analysis has certain limitations: many articles may lack keywords, or the keywords provided may be selected from a predefined list, making it difficult to accurately reflect the actual content and research focus of the articles [15]. With the rapid advancement of artificial intelligence in recent years, traditional literature analysis methods such as keyword networks have gradually struggled to handle high-dimensional data and address complex problems. Consequently, researchers have turned to natural language processing (NLP) methods to study scientific documents in greater depth, providing more comprehensive support for scientific research. Among these techniques, the LDA topic model, a prominent text mining tool, has garnered significant attention in academia [16; 17]. The LDA topic model can identify latent topics within large-scale text data, aiding researchers in extracting key research directions and development trends from extensive literature [18]. Compared to traditional qualitative literature reviews, the LDA model provides higher systematization and objectivity in a data-driven manner, capable of revealing hidden thematic structures.  
The advantages of LDA topic modeling have been widely applied across various disciplines, especially in research that requires the analysis of large text datasets, such as sociology and education [19; 20]. [PERSON] et al. [17] have also demonstrated that the LDA model can effectively identify hidden topics within large-scale documents by analyzing word frequency and distribution patterns. It has been extensively applied in complex fields such as software engineering, political science, healthcare, and linguistics and is suitable for a variety of contexts, including social media and academic research.  
In this study, we apply the LDA topic model to analyze research directions on AI in physical education, providing a more systematic perspective for research and practice in the field. Compared to traditional methods, the LDA model reveals latent thematic structures, helping to capture AI application trends and its future potential comprehensively. This study aims to provide theoretical support for the intelligent development of physical education and to encourage further research in this area.