Big Data-Driven Analysis of Stylistic Evolution in Ancient Chinese Literature

This study pioneers a digital humanities approach to systematically examine the millennium-spanning evolution of Chinese literary style. By constructing a cross-generational corpus from the pre-Qin through the Ming-Qing periods and developing a multidimensional quantitative index system, it integrates linguistic feature extraction with social network analysis to overcome the limitations of traditional chronological methods. Methodological innovations include dynamic time-window processing and an enhanced latent Dirichlet allocation model, which reveal key transition patterns between poetry and prose. Findings identify significant “style interpenetration” during the Tang-Song transformation and underscore the previously underestimated normative influence of imperial examination reforms, whose effects surpassed those of dynastic transitions. These insights challenge conventional literary historiography while providing new perspectives on the dynamic interplay between literary production and social structures.