An effective detection scheme based on decoding electroencephalogram (EEG) signals under different states of sickness is proposed, which is helpful for studying methods to alleviate virtual reality motion sickness (VRMS). This article uses multivariate variational mode decomposition (MVMD) to divide EEG into five frequency bands and divides the data into different sickness state groups based on the results of the motion sickness scale. The phase-locking value (PLV) method is used to calculate the functional connections within and between EEG frequency bands to construct a super adjacent matrix, and classification recognition is performed based on models such as support vector machine (SVM) and convolutional neural network (CNN). The research results show that the fusion of three topological features with significant differences in clustering coefficient, local efficiency, and weighted node degree results in sickness vs Non sickness, high sickness vs The highest average classification accuracy in the two tasks of low sickness was 91.70% and 96.00%, respectively. In addition, this article also directly inputs the super adjacency matrix into the CNN model, achieving average classification accuracies of 93.40% and 98.50% in two tasks, respectively. The results indicate that the method proposed in this study can be used for the detection of motion sickness in virtual reality and provide reference for further research on the impact of motion sickness on the functional coupling of various brain regions.