Knowledge Analysis and Semantic Recognition for Multimedia Technology Courses Using Knowledge Graph

Smart education has changed teaching ways and methods. However, there is a lack of effective research on the relationship among different knowledge points. To address problems such as unclear relationship among different knowledge points, poor continuity of teaching process, rich but scattered teaching resources. Therefore, we propose to a curriculum knowledge point model through curriculum Knowledge Graph technology, analyzes and studies knowledge points from the perspective of curriculum, and uses the Python technology to grasp the key curriculum information. Then, this paper uses the Jieba Library in Python to complete Chinese word segmentation, extract key knowledge points and mine various associations between knowledge points. Finally, this paper completes the visualization of Knowledge Graph through Gephi. The results show that the effectiveness of this method is verified on two groups of data and lay a foundation for the further design of personalized teaching path, question and answer system, and student group portrait.