Generation of Personalized Knowledge Graphs Based on GCN

Education must follow the principle of teaching students in accordance with their aptitude. In this paper, we propose a novel method to generate personalized knowledge graphs based on graph convolutional network. We have summarized methods evaluating difficulty exercises, and apply them generation graph. After that, adjacency matrix corresponding eigenvectors nodes are used as inputs network, semi-supervised leaning node classification is adopted continuously iterate training optimize convolution neural network model. Meanwhile, for each student, more accurate services can be provided. The experimental results show that our make better realize in-depth services.