High-quality video semantic segmentation has made great progress thanks to advanced deep neural networks. However, two main challenges persist: 1) achieving high-precision segmentation without excessive computational costs, and 2) ensuring semantic consistency in segmentation results. Recent studies have predominantly employed self-attention mechanisms to merge similar features across multiple frames. This fusion typically occurs after deep feature extraction. However, this approach overlooks similar features within individual frames and suffers from information loss during extraction, leading to insufficient fusion of similar features. This, in turn, impacts segmentation accuracy and causes semantic inconsistencies. To address these issues, we propose a novel neural network with Multi-level Global-Local Context Aggregation (MGLA) in this paper. MGLA models similar features both across multiple frames and within a single frame as global information, thus ensuring semantic consistency in the segmentation results. Moreover, MGLA incorporates multi-level multi-frame feature information in the fusion process, complementing each other to prevent information loss and ensuring segmentation accuracy. The proposed method has achieved state-of-the-art results on two challenging public video semantic segmentation datasets: 78.9% on CamVid and 81.2% on Cityscapes.