Artificial intelligence technology has risen to become a national strategic pillar. Its foundational course Introduction to Artificial Intelligence not only serves as an enlightening link in AI talent cultivation, but also is one of the first core courses for the establishment of virtual teaching and research offices by the Ministry of Education. Faced with issues such as a complex knowledge system in teaching content and abstract concepts, new requirements are placed on teaching design and learning strategies. Based on the semantic network characteristics of knowledge graphs, this study focuses on constructing a multi-dimensional knowledge representation system for this course. Leveraging the structural and dynamic knowledge organization advantages of knowledge graphs, it systematically organizes the core knowledge units and associated networks of the course, explains its core architecture, and focuses on breaking through bottlenecks such as knowledge discretization and cognitive superficiality in traditional teaching modes. Against this backdrop, the research results will provide innovative methodological support for the optimization of the curriculum construction system for data science and big data technology majors and the digital transformation of education.