ing at solving the problems of low segmentation accuracy in the automatic segmentation of brain white matter hyperintensity region on magnetic resonance imaging brain images and easy to miss small lesions, a U-Net segmentation model combining attention and inception is proposed. In the coding stage of U-NET, the Inception module is added to increase the width of the network, so that it has the ability to extract multi-scale features, and the attention module is added to enhance the attention of the network to the segmentation target. The addition and fusion of the two can effectively improve the feature extraction and expression capabilities of the network. Simultaneously, adding residual connections on each convolutional layer in the decoding stage can improve the optimization speed of the network. In addition, because of the problem that sample imbalance easily leads to too many false negatives in the segmentation results, the Tversky loss function with balance adjustment ability is employed to optimize network training. The experimental results show that the proposed method can segment brain white matter hyperintensity region, especially the small lesion area, and each segmentation index is better than those of multiple comparison methods.