A glioblastoma multiforme (GBM) multi-modal magnetic resonance (MR) image automated segmentation algorithm based on hybrid features and prior knowledge was proposed, as most traditional GBM multi-modal MR image segmentation algorithms failed to subdivide the whole tumor into different sub-regions. The head region was adjusted to the approximate unrotated position once the GBM multi-modal MR image was registered, and the bias field correction was performed by the N4ITK method. A random forest classifier was applied to initially segment GBM multi-modal MR image after the extraction of the local location features, intensity features, texture features, symmetric features and contextual features of GBM multi-modal MR image. The final segmentation results were obtained by removing small regions and median filtering, based on the prior knowledge of the anatomical structure of GBM tumor. The Dice similarity coefficient was adopted as an evaluation metric, and the average Dice similarity coefficient values were 0.871 and 0.882 for segmenting the whole tumor in TCGA-GBM and CH-GBM databases by the proposed algorithm, respectively. Results indicated that the proposed method is suitable for clinical application of GBM multi-modal MR image segmentation task with relative high accuracy.