To address the problems of complex backgrounds， significant variations in lesion scale， and subtle differences between adjacent categories in multi-level recognition of wheat stripe rust， a Multi-Scale feature fusion and Attention-enhanced Residual Network （MSA-ResNet） was proposed. First， residual connections were employed to alleviate the gradient vanishing during deep network training and improve the feature representation capability of the model. Second， a top-down multi-scale feature fusion strategy was adopted to enhance the consistency of features at different scales through semantic alignment， thereby improving the model’s adaptability to lesion size variations and morphological complexity. Finally， a Convolutional Block Attention Module （CBAM）， integrating channel and spatial attention mechanisms， was introduced to enhance feature responses in key lesion regions and suppress interference from background elements such as weeds and soil. Experimental results on the Yellow-Rust-19 public dataset and a self-built dataset show that MSA-ResNet achieves the accuracies of 95.67% and 97.17%， respectively， outperforming EfficientNet-CBAM by 1.67 and 0.84 percentage points， respectively. These results indicate that the proposed MSA-ResNet better meets practical application requirements and provides a superior solution for multi-level recognition of wheat stripe rust under field conditions.