Research on the Construction of Digital Knowledge Graphs Based on Resources of National First-Class Undergraduate Programs

[Purpose/Significance]: The digitalization of education is an essential path to advancing higher education. The construction of knowledge graphs is a key approach to achieving the digitalization and intelligence of education. [Method/Process]: This paper leverages the rich video resources of existing national first-class undergraduate programs and, based on the teaching orientations of different universities, independently designs customized ontologies and extraction principles. These are then integrated into the LLM knowledge graph builder to ensure the hierarchical structure of the overall course framework. The course video content is transformed into text form, and large language models (LLMS) and word segmentation tools are used for core content extraction, text cleaning, and lexical analysis. The structured text is then converted into SPO (Subject-Predicate-Object) triplets database. [Results/Conclusions]: Finally, the database is imported into the LLM knowledge graph builder, which is pre-configured with extraction rules. It will automatically generate the knowledge graph. After the text is imported into the LLM knowledge graph builder, it will be manually checked to ensure it better meets the actual needs of the students. [Innovation/Limitations]: The research team plans to apply the knowledge graph to train a specialized knowledge-based Q&A assistant. This will support students' understanding and self-assessment of knowledge points in an online learning community. Student feedback will be used to improve and enrich the knowledge graph. Compared to existing methods, this approach better aligns with the constantly evolving digital teaching resources available online, offering more comprehensive and higher-level automation.