Rock thin-section microscopic images frequently exhibit complex local textures, blurriness, and high noise levels, posing significant challenges for traditional feature extraction and matching algorithms. These methods often fail to identify effective feature points in high-resolution rock thin-section images, hindering the realization of panoramic stitching while also resulting in slow processing speeds. To address the aforementioned issues, a rock thin- section microscopic image stitching method based on an improved GLU-Net has been proposed. This method integrates an enhanced correlation computation module to improve global and local correspondence, employs a Feature Pyramid Network for multi-scale feature fusion, incorporates a designed adaptive convolutional attention mechanism to optimize attention to key regions, utilizes global and local decoders to obtain optical flow, and applies homography transformation for image stitching, thereby constructing a novel image stitching network model. Experimental results demonstrate that, compared to traditional image stitching algorithms and other classical image stitching network models, the proposed network achieves superior stitching performance. In stitching tests on a self-constructed dataset, a stitching accuracy of 86.75% has been attained with an average registration time of 0.394 seconds per pair, effectively balancing enhanced accuracy with processing efficiency.