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A multi-scale feature aggregation method based on multi-scale attention is proposed to address the problem of low recognition accuracy in automatic segmentation of stroke lesions in brain magnetic resonance images due to complex segmentation target edges and diverse scale changes. This method utilizes attention mechanisms to adjust the weights of different channels of intermediate features and adaptively selects features of different scales for fusion. A series of experiments conducted on the public dataset ATLAS for ischemic stroke and selected Dice coefficient, Hausdorff distance, overlap, accuracy and recall as evaluation indicators. The result demonstrate that the proposed model achieves leading segmentation performance in stroke lesion segmentation. Additionally, the model also conducted comparative experiments on the brain tumor dataset publicly available on Kaggle, proving its good generalization ability.