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.

翻译：人工智能（AI）的快速发展推动了教育领域的重大变革。在众多AI技术中，基于自然语言处理（NLP）的知识图谱（KG）已成为整合多源信息的可视化工具。针对大学教育中众多专业课程与复杂学习资源导致学生学习效果不佳的问题，本文提出了一种面向电子信息专业的自动化知识抽取、可视化知识图谱构建及图谱融合框架。通过数据挖掘分析课程间的关联度与关系，对热点知识概念进行排序，并探索学科交叉领域。研究旨在提升学生学习效率，探索AI赋能的新型教育范式。该框架有望帮助学生全面理解各概念与课程之间的内在联系，从而更深入地认知专业领域的知识体系。