Applying Text Mining Technology in Mining Thematic Trends in Chinese Literature

Abstract

Combining text mining technology with mining themes has emerged as a valuable tool for analyzing Chinese literature. This work presents a novel technique to quantify the features of text mining by merging mining algorithms. Determining the significance and language of ancient Chinese literature becomes less subjective as a result. This work demonstrates that the model can classify text mining words in Chinese literature accurately, up to the maximum degree of precision. The study proposes the use of text mining to analyze thematic trends in Chinese literature, employing a model known as the Enhanced Hierarchical Based Gradient Boost Algorithm (TEHGBA). For every Chinese literature outcome, the new TEHGBA model receives higher marks. According to the study's findings, the suggested model produced outcomes with 98.6% accuracy, 95.7% precision, and 90% recall. The study comes to the conclusion that the suggested model aids in the analysis of Chinese literary works and yields excellent recall, accuracy, and precision outcomes.