Motion sickness has been a key factor affecting the virtual reality user experience and limiting the growth of the virtual reality industry. To address this issue， this paper investigates the effects of virtual reality motion sickness on neural activity in the brain and uses electroencephalogram （EEG） features to detect levels of motion sickness. To obtain features that can measure the level of vertigo， this paper records the EEG signals of subjects before and during the experience of the vertigo test scene， calculates the rhythm energy and fuzzy entropy， uses statistical analysis for feature selection， and finally classifies and verifies the validity of the features. The results show that the energy in the θ and α bands of CP4 and Oz and the energy in the β and γ bands of C4 are significantly reduced when subjects develop motion sickness （p<0.01）； in terms of fuzzy entropy， there are significantly higher values of FC4 and Cz fuzzy entropy in the δ band （p<0.000 1） and significantly lower values of O1 fuzzy entropy in the β band （p< 0.000 1）. Compared to linear discriminant analysis （LDA）， logistic regression （LR） and support vector machine （SVM）， K nearest neighbor （KNN） shows better classification results with 89% and 91% classification accuracy on rhythm energy and fuzzy entropy， respectively. This study shows that EEG rhythm energy and fuzzy entropy are expected to be effective indicators for motion sickness level detection， providing an objective basis for studying the causes of virtual reality motion sickness and mitigation options.