Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the great success of deep learning technology, CNNs based methods showing superior performance in image segmentation. However, these methods rely on a large number of human annotations, which are expensive to collect. In this paper, we propose a deep unsupervised method for image segmentation, which borrowed the ideas of classical graph partitioning. Our approach contains the following two stages. First, a Superpixel Guided Autoencoder (SGAE) is designed to learn the deep embedding and smooth the image simultaneously, then the smoothed image passed to generate superpixels. Second, based on the learned embedding, we propose a novel segmentation algorithm called Deep Superpixel Cut(DSC), which measures the deep similarity between superpixels and then adaptively partitions the superpixels into perceptual regions. Experimental results on the BSDS500 dataset demonstrate the effectiveness of the proposed method