This research proposes a multi-scale attention feature fusion stereo matching algorithm (MGNet) to address the mismatching phenomenon of the current end-to-end stereo matching algorithm in challenging and complex scenes. A lightweight group-related attention module was designed. This module uses group-related fusion units to effectively combine the spatial and channel attention mechanisms while capturing rich global context information and long-distance channel dependencies. The designed multi-scale convolutional global attention module can process local information and global information at multiple scales, add non-local operations in the global feature processing stage. The module captures multi-scale and global contexts simultaneously, providing rich semantic information. In the cost aggregation stage, channel attention was introduced to suppress ambiguous matching information and extract differentiated information. Three datasets were used to analyze the proposed algorithm’s effectiveness. The results indicate that the proposed algorithm performs effectively in morbid areas like thin structures, reflective areas, weak textures, and repeating textures.