Research on Named Entity Recognition Technology of Knowledge Graph for Flipped Classroom

Knowledge Graph is a kind of storage form that uses graphic models to store entities and the relationship between entities. In the process of building Knowledge Graph for flipped classroom, it is necessary to use Named Entity Recognition technology to identify keywords in textbooks and generate entities in Knowledge Graph. In the process of calculating the Position Vectors of words after segmentation, some laws that can be used for Chinese Named Entity Recognition. Based on these laws, a named entity recognition algorithm based on the textbook position vectors is designed without training set. The test results show that the Precision of the algorithm is 90.59% and the Recall is 91.73%, and the algorithm, to some extent, can recognize nested named entities.