Appearance-based gaze estimation aims to infer the gaze direction of the eyes from facial images. Current models face challenges in extracting both local and global information from facial images, which is often disrupted by background noise and facial appearance, leading to reduced prediction accuracy. To address this, a hybrid Transformer model was proposed, combining multi-scale feature aggregation with a shared attention mechanism. Preliminary feature extraction was performed using convolutional block attention module(CBAM) channel-space attention in combination with ResNet to enhance the model's sensitivity to the eye region. A multi-scale feature fusion module was introduced to integrate information at different scales, capturing coarse-grained features, such as head pose and facial expressions, while focusing on fine-grained details like the eyes. This improves the model's ability to express multi-level features. To extract global information, a Transformer module with positional encoding was used to capture global dependencies in the images and model spatial information, allowing for a more comprehensive understanding of the structure and layout of the images. An improved shared attention mechanism was then employed to reduce the focus on irrelevant features by leveraging the similarity between images. Experimental results show that the proposed model achieves outstanding performance on the Gaze360, MPIIFaceGaze, and RT-Gene datasets, with an average angular error reduction of 0.11° and 0.12°, respectively, demonstrating a significant advantage in gaze estimation tasks.