As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide personalized guidance, and thus, achieve the purpose of artificial intelligence-assisted education. However, existing methods, for instance, convolutional knowledge tracing, lacking consideration of graph structure and multivariate time-series prediction, result in poor prediction accuracy. Inspired by recent successes of the graph neural network (GNN), we present a novel multivariate graph knowledge tracking (MVGKT) framework to address these limitations. Specifically, a multivariate time-series knowledge tracking system based on spatio-temporal GNN is designed to model student learning trajectories in different spatial and temporal dimensions and capture both temporal dependencies and interstudent correlations. MVGKT incorporates a gate recurrent unit attentive mechanism and graph Fourier transform, discrete Fourier transform, and graph convolution network to increase the predictive accuracy of the student performances. In addition, we design a question difficulty extraction system to obtain information on the difficulty of the questions, and thus, enhance the data features. Numerous experiments on the ASSISTments dataset have demonstrated that MVGKT is superior to existing knowledge-tracking methods on four metrics and has shown that our approach can enhance the predictive accuracy of student performance.