Abstract: There are noises and speckles in OCT retinal images, and a single extraction of spatial features is often easy to miss some important information. Therefore, the target region cannot be accurately segmented. OCT images themselves have spectral frequency domain characteristics. Aiming at the frequency domain characteristics of OCT images, this paper proposes a new dual encoder model based on U-Net and fast Fourier convolution to improve the segmentation performance of the retinal layer and liquid in OCT images. The proposed frequency encoder can extract image frequency domain information and convert it into spatial information through fast Fourier convolution. The lack of feature information that can be omitted by a single space encoder will be well-complemented. After comparison with other classical models and ablation experiments, the results show that with the addition of a frequency domain encoder, the model can effectively improve the segmentation performance of the retinal layer and liquid. Both average Dice coefficient and mIoU are increased by 2% compared with U-Net. They are increased by 8% and 4% compared with ReLayNet, respectively. Among them, the improvement of liquid segszmentation is particularly obvious, and the Dice coefficient is increased by 10% compared with the U-Net model.