Glioma is the most common primary brain tumor, and it's extremely dangerous for the patient's health. Magnetic Resonance Imaging (MRI) based glioma segmentation is an important tool for doctors to use when examining, analyzing, and diagnosing glioma's outward appearance both for the indoor and outdoor patients. Among other methods, deep learning-based glioma segmentation is the most widely used in literature. Therefore, in this paper, we have proposed glioma segmentation methods which is primarily based on deep learning more specifically convolution neural networks. A DM-DA-based cascade method for glioma segmentation based on 2DResUnet is proposed to address the problem of insufficient 3D spatial information acquisition in 2D full convolutional networks and the memory consumption problem of 3D full convolutional networks. Use of multiple-scale fusion mechanisms, DenseBlock, Attention, and multi-scale convolutional networks to segment gliomas at different stages and fixed region sampling to reduce the model's three-dimensionality in the Unet model using the glioma image's multi-sequence information, the convolutional network can better segment the tumor by using less memory. Use the BraTS18 data set for local five-fold cross-validation, and use the BraTS17 data set for official online evaluation. The evaluation results show that the average Dice Score of the edema, core and enhancement areas of the glioma segmentation results on the BraTS17 validation set by DM-DA-Unet achieves higher performance. The average sensitivity is also high, which is close to the best model segmentation effect on the BraTS17 validation set, and can accurately segment gliomas.