With the development of information technology and big data analysis technology, data mining has been widely used in various disciplines. In this paper, the literary themes and their trend changes in Chinese language literature are explored through big data analysis techniques, combined with the LDA theme model. The study used CiteSpace and other tools to analyze the relevant literature from 2000 to 2023, identify the major literary themes in the field of Chinese language literature, and discuss their trends in depth. It is found that the literary themes of Chinese language literature mainly focus on love, war, social reality, etc., and these themes show different research hotspots and trends in different time periods. For example, the number of literature publications reached 100 in 2020, which is 25 times more than the number of publications in 2000, showing the rapid development of this field in recent years. In addition, keyword analysis shows that “war”, “loyalty” and “society” are the main research concerns. Further, through the co-occurrence analysis of the literature, the changing patterns of literary themes are identified, providing new perspectives and directions for the study of Chinese language literary works. This paper provides strong evidence for academic research on Chinese language literature through data mining methods, revealing the major trends and hot topics in its development.
