The problem of retinal blood vessel segmentation, such as limited labeled image data, complex blood vessel structure with different scales, and easy to be disturbed by the lesion area, is a concern for researchers. Thus, to address this problem, the study proposes a multiscale dense attention network for retinal blood vessel segmentation. First, based on U-Net architecture, the concurrent spatial and channel squeeze and channel excitation attention dense block (scSE-DB) is used to replace the traditional convolution layer, strengthening the feature propagation ability, and obtaining a dual calibration for feature information so that the model can better identify blood vessel pixels. Second, a cascade hole convolution module is embedded at the bottom of the network to capture multiscale vascular feature information and improve the network's ability to obtain deep semantic features. Finally, we performed the experiments on three datasets (DRIVE dataset, CHASE_DB1 dataset, and STARE dataset), and the results show that the accuracy of the proposed network is 96.50%, 96.62%, and 96.75%; the sensitivity is 84.17%, 83.34%, and 80.39%, and the specificity is 98.22%, 97.95%, and 98.67%, respectively. Generally, the results show that the segmentation performance of the proposed network outperforms that of other advanced algorithms.