Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.