In order to solve rough contour of some blood vessels and the loss of vessel-perpherals and branches in traditional retinal vessel segmentation, a novel method forretinal vessel segmentation which combines linear spectral clustering super-pixel with generative adversarial networks (GAN) is proposed.The accuracy of segmentation is improved using the multi-scale features from atrousspatial pyramid pooling (ASPP) module with a modified GAN method. After the segmentation image is obtained, by utilizing the characteristics of high edge suitability and clear contour of linear spectral clustering super-pixel segmentation, the GAN output image was mapped to the super-pixel segmentation image. The segmentation was achieved by classifying the pixel clusters. The experimental results show that compared with the traditional retinal vessel segmentation method, the sensitivity and accuracy of the proposed method are improved, especially in the details of the contour edge.