The Application of Knowledge Graph in Experimental Teaching of Electronics Majors

The rapid development of modern science and technology such as big data technology and information technology is constantly driving changes and progress in the field of education, especially the application of artificial intelligence technology, which has brought new changes to traditional educational concepts, models, and systems. In the research boom of smart education, experimental education is gradually moving towards the path of intelligent development. To improve the teaching quality of online experiments and improve the guidance system of online experiments, this paper is based on electronic circuit basic course experiments. Artificial intelligence technology is applied to the guidance process of online experiments, and the platform's user data and teaching resources are used to provide accurate, standardized, and personalized online experiment guidance for experimental users. A knowledge graph based experimental knowledge recommendation model is designed, which uses collaborative filtering recommendation algorithm to analyze the historical operation records of all users on the platform, calculate the similarity between various experimental operation errors, and then fuse the semantic connotation of experimental faults in the knowledge graph. Knowledge graph based experimental fault similarity is introduced, providing users with accurate and personalized experimental guidance. This model can better predict user experimental behavior and greatly improve the accuracy of recommendations while considering both user operations and intrinsic knowledge.