Objective：The current machine learning was performed based on the radiomic features of contrast - enhanced T1 - weighted imaging(T1WI+C)of glioma，which was used to construct a prediction model of Ki67 index for predicting the proliferation activity of glioma cells. Methods：A retrospective analysis onsisted 113 patients with glioma，which were confirmed by the surgical and pathological results of our hospital and the Ki67 index defined by immunohistochemistry. The training set and the test set were divided into 8∶2. The glial region of interest(ROI)was hand-painted by MRIcroGL software，1 338 image features were obtained by using the pyradiomics module in Python，and the best image features were obtained through t-test and the least absolute shrinkage and selection operator(Lasso)regression algorithm. Furthermore，using the support vector machine classifier to build the Ki67 prediction model based on the best features，using the receiver operating characteristic(ROC)curve and the calibration curve to evaluate the predictive performance of the model. Results：A total of 1 338 radiomics features were extracted from T1WI+C images of each patient，and six features closely related to glioma ki67 index were screened out after dimensionality reduction. Based on the support vector machine algorithm model，the AUC in the training set of Ki67 index prediction was 0.82，and the accuracy rate was 0.72；in the test set of Ki67 index prediction，the AUC was 0.91，and the accuracy rate was 0.83. The results of the calibration curve of the model showed that the difference was not statistically significant(Brier score=0.175). Conclusion：The prediction model of support vector machine that established on the learning of the T1W1+C imaging omics characteristics may have a better predictive effert on the proliferation activity of glioma cells，and it may help the selection of individual diagnosis and treatment plans for patients and the development of precision medical care in the future.