Deep learning has become one of the important tools for hyperspectral image classification due to its modular design and powerful feature extraction capability. However, effectively extracting deeper features and simultaneously improving the analysis of spatial and spectral joint features remains an urgent challenge. In response to these issues, a deep feature extraction residual network is proposed in this paper, composed of two key components: a multi-level transfer fusion residual network and a spatial-spectral multi-resolution fusion attention residual network. The multi-level transfer fusion residual network effectively promotes interaction between feature information to obtain deeper-level features. Subsequently, the spatial-spectral multi-resolution fusion attention residual network ensures comprehensive extraction of spatial-spectral joint features and multi-resolution features from hyperspectral data. To validate its effectiveness, the performance of the proposed method was evaluated on three hyperspectral datasets, Indian Pines, Pavia University, and Salinas Valley, achieving classification accuracies of 98.10%, 99.81%, and 99.94% respectively. Experimental results demonstrate that, compared to other methods, this network exhibits better generalization capability and classification performance.