Change detection in SAR images is an important but challenge task. Due to the difficulty of SAR interpretation, reliable training samples are lacking, limiting the application of deep learning technology in SAR image change detection. To overcome this problem, this article proposes an unsupervised SAR image change detection method based on slow feature analysis theory with convolutional neural network (SAR-SFAnet). It adopts SDAEs to automatically extract features from SAR data, and employs slow feature analysis theory to project the extracted multi -dimensional features into a new space. In addition, an alternative optimization strategy is introduced, making the features learned by bi - temporal stacked denoising auto-encoder (SDAEs) have more consistent representations, as well as making the change detection map more accurate. Finally, comparative experiments are carried out on two real SAR data sets, demonstrating the effectiveness of the proposed method.