Gaze estimation, which predicts the position or direction of human eye gaze, plays a crucial role in applications of human-computer interaction and computer vision. To address the issues of feature diversity and incomplete utilization, this thesis proposes a dual-branch feature fusion gaze estimation algorithm. Firstly, a dual-branch network model combining Agent Swin Transformer network with residual network is constructed to extract gaze features. The improved Agent Swin Transformer network forms the global feature extraction branch, extracting global semantic features layer by layer, while the residual network forms the local feature extraction branch, extracting local detailed features at different scales. Through feature fusion, the feature tensors are concatenated to enhance the model"s representation capability. Secondly, the Agent Swin Transformer network integrates the EMA module and SCConv module to strengthen features, maintain information effectiveness, and reduce complexity and computational costs. Finally, combined with head pose estimation, the gaze direction is estimated to mitigate the influence of interfering factors on eye appearance. Extensive experiments on the MPIIFaceGaze dataset demonstrate that the proposed method achieves an average gaze estimation angle error of 4.23°. Compared with current mainstream methods of similar kind, the proposed algorithm achieves more accurate gaze estimation.