Objective Diabetic retinopathy (DR) is a serious complication of diabetes that may cause vision loss or even blindness in patients. Early examination of the choroid plays an essential role in the diagnosis of DR. However, owing to the fuzzy choroid-sclera interface (CSI) and shadow of retinopathy in the optical coherence tomography (OCT) images of DR, most existing algorithms cannot segment the choroid layer precisely.Methods In this paper, we propose an optimized squeeze-excitation-connection (SEC) module integrated with the UNet, called the SEC-UNet, which not only focuses on the target but also jumps out of the local optimum to enhance the overall expressive ability. The present paper aims to improve the accuracy of choroid segmentation in DR OCT images.Results The experimental results show that the area under the ROC curve (AUC) of the SEC-UNet reaches up to 0.993 0, which outperforms that obtained for conventional UNet and SE-UNet models. It indicates that the SEC-UNet can obtain accurate and complete segmentation results of the choroid layer. Statistical analysis of choroid parameter changes indicated that compared with normal eyes, the 1 mm adjacent area of choroid fovea increased in 87.1% of DR patients. It proved that DR is likely to cause choroid layer thickening.Conclusion Our method may become a useful diagnostic tool for doctors to explore the function of the choroid in the prevention, pathogenesis, and prognosis of diabetic eye disease.