To address the problem that existing convolutional neural networks have difficulty in capturing long-range dependencies in human faces, a dual-branch residual network based on large convolution kernels is proposed and applied to face age estimation. Firstly, to overcome the limitation of small receptive fields of traditional small convolution kernels, large convolution kernels are adopted to improve the residual module of the deep learning model (ResNet), thereby expanding the effective receptive field of the network and more efficiently capturing the global information and long-range dependencies in face images. Secondly, considering the crucial role of facial fine features in age estimation, a detail downsampling module is introduced, which can minimize the loss of detailed information at the initial stage of the network. In the design of the network structure, the original residual module of ResNet and the improved large convolution residual module are innovatively connected in parallel to form a dual-branch residual network, and an attention module is utilized to achieve feature fusion between the two branches. To further enhance the deep mining of face age features, two large convolution residual mo-dules are concatenated after the dual-branch residual network, and through progressive feature abstraction, the model's ability to model complex age patterns is strengthened. Finally, in response to the challenges brought by the label ordinality characteristics in age estimation, the constructed dual-branch residual network is combined with ordinal regression methods. By converting age values into ordered label sequences for modeling, the model's ability to distinguish age changes is effectively improved. Experimental results show that the proposed method reduces the mean absolute error (MAE) by up to 0.46 on the UTK-FACE dataset and by up to 0.09 on the FG-NET dataset.