Design and Implementation of an Intelligent Question-Answering System Based on a Chinese Literature Knowledge Graph

Chinese literature is an important part of Chinese culture, carrying rich historical, cultural, and philosophical connotations, and holds unique academic value and social significance. However, with the continuous deepening of Chinese literature research, traditional knowledge retrieval and research methods often fail to meet the modern users' demand for efficiency and intelligence. How to quickly and accurately obtain knowledge about Chinese literature has become an urgent issue to address. Against this background, an intelligent question-answering system based on the Chinese literature knowledge graph has emerged. This paper takes Dream of the Red Chamber as the research object, constructs a knowledge graph of Chinese literary works, and designs and implements an intelligent question-answering system based on it. By using deep learning algorithms such as CNN, LSTM, and BiLSTM, combined with natural language processing and knowledge graph technologies, it explores how to extract important elements such as characters, events, and emotions from literary works. Finally, through the designed intelligent question-answering system, and with the BiLSTM model achieving the best evaluation, where Precision is 96.28 %, Recall is 93.60 %, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F 1$</tex> is 93.07 %, the system is able to deeply mine and analyze key elements in Dream of the Red Chamber, promoting the digitalization and intelligence of literary research.