Optical coherence tomography (OCT) technology can obtain a clear retinal structure map, which is greatly beneficial for the diagnosis of retinopathy. Ophthalmologists can use OCT technology to analyze information about the retina's internal structure and changes in retinal thickness. Therefore, segmentation of retinal layers in images and screening for retinal diseases have become important goals in OCT scanning. In this paper, we propose the multiscale dual attention (MSDA)-UNet network, an MSDA mechanism network for OCT lesion area segmentation. The MSDA-UNet network introduces position and multiscale channel attention modules to calculate a global reference for each pixel prediction. The network can extract the lesion area information of OCT images of different scales and perform end-to-end segmentation of the OCT retinopathy area. The network framework was trained and tested on the same OCT dataset and compared with other OCT fluid segmentation methods to assess its effectiveness.