Digital Technology Empowers Modern Chinese Literature Research: An Analysis of Thematic Evolution in Classic Works Based on Text Mining

Abstract

With the rapid development of digital humanities, traditional research on modern Chinese literature is constrained by limited samples and subjective interpretation, while text mining provides a quantitative and systematic analytical approach. This study takes 60 classic works of modern Chinese literature across three historical periods, namely 1919- 1949, 1949-1978, and 1978 to the present, as research objects and constructs an integrated text-mining framework covering data preprocessing, LDA topic modeling, and visual analytics. By combining computational analysis with traditional literary criticism, the study conducts an empirical investigation of cross-period thematic evolution. Eight core themes, including family-nation sentiment, human-nature exploration, and revolutionary narrative, are identified, and their evolutionary trajectories are systematically mapped. The results show that social-historical context, shifts in literary movements, and changes in authors’ creative orientations are the main driving forces behind thematic transformation. The findings demonstrate that text mining can objectively and dynamically represent thematic evolution in modern Chinese literature. The study further argues that complementary integration of computational methods and humanistic inquiry is an important path for literary research in the digital age.