Research on the Construction of Knowledge Map Technology Framework of Core Curriculum within the "AI+" Context

Abstract

With the deep integration of artificial intelligence into education, core courses face challenges like the need for strong interdisciplinary knowledge, complex content, and a lack of adaptability in teaching. As a result, this research utilizes “AI+” as the technological framework, constructing a knowledge graph based on a three-tier architecture-“data layer, schema layer, application layer”-utilizing the Protege tool for ontology construction and standardized coding. By combining the Bert model, precise extraction of entity relationships is possible, which facilitates knowledge storage and the development of a visual interface via the Neo4j graph database. This knowledge graph ensures quality through a closed-loop mechanism of “construction-evaluation-optimization-application” supporting intelligent applications such as personalized learning recommendations, smart question-answering, and teaching decision-making support. It effectively mitigates the fragmentation of interdisciplinary knowledge, drives educational model innovation, and provides a technical paradigm and practical reference for implementing “AI+Education”.