  The field of education has undergone a significant transformation due to the
rapid advancements in Artificial Intelligence (AI). Among the various AI
technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP)
have emerged as powerful visualization tools for integrating multifaceted
information. In the context of university education, the availability of
numerous specialized courses and complicated learning resources often leads to
inferior learning outcomes for students. In this paper, we propose an automated
framework for knowledge extraction, visual KG construction, and graph fusion,
tailored for the major of Electronic Information. Furthermore, we perform data
analysis to investigate the correlation degree and relationship between
courses, rank hot knowledge concepts, and explore the intersection of courses.
Our objective is to enhance the learning efficiency of students and to explore
new educational paradigms enabled by AI. The proposed framework is expected to
enable students to better understand and appreciate the intricacies of their
field of study by providing them with a comprehensive understanding of the
relationships between the various concepts and courses.
