Mining Educational Value of Visualization of Sentiment Classification in Ancient Chinese Literature

This research used deep learning technology to conduct sentiment classification on ancient Chinese literary works and presents the results through visualization. The study collected texts from multiple periods and genres and constructed a deep learning model. The overall accuracy of sentiment classification of the model reached 85%. It has its own advantages and disadvantages in classifying works from different genres and dynasties, and there are multiple factors leading to misjudgments. A combination of multiple tools was adopted for visualization. According to user feedback, the system has value in academic research and visualization display but needs to be optimized. This research provides new methods for the study of ancient literature, etc. In the future, the system will be continuously improved upon, and external integration will be strengthened.