Research on the Integration of AI Ancient Literature Models in College Chinese Teaching Practice

Abstract

In recent years, ancient literature has come to be used for an extensive variety of publications, along with poetry, dramas, religious scriptures, and more. The use of artificial intelligence (AI) models to teach historical Chinese literature in a college-level Chinese path. Early Chinese literature and its rich historical heritage and historical importance, offer particular challenges and opportunities for each educator and student. College students' understanding is greatly impacted by the use of ancient literature models, one of the most important skills in Chinese teaching. Students could have a great perception of Chinese concepts and discover ways to connect others with comfort as an outcome. The study's goal is to demonstrate how AI models of historical literature may be included in college Chinese teaching practices. This study proposed a novel drosophila food search fine-tuned intelligent recurrent neural network (DFS-IRNN) to predict the functionality of Chinese teaching practice in college students. This study collects a diverse set of historic Chinese literature texts, consisting of philosophical works. The data become preprocessed using Z-score normalization for the obtained information. By selecting effective teaching techniques and materials based on the outcomes anticipated using the IRNN model, DFS may be used to optimize the layout and delivery of Chinese literary publications. The suggested method scored 98% in critical thinking and 98% in performance ratio. The proposed approach, AI ancient literature methods, is used within the Chinese teaching Implementation. The suggested technique is evaluated in various performance metrics. The result validated that the suggested method outperformed traditional algorithms.