To overcome the high sensitivity of the traditional Markov random field model to the speckle noise of synthetic aperture radar (SAR) images and the blurring of the oil spill boundary identification in marine oil spill identification, this study proposes an edge-constrained hidden Markov random fields (HMRF) at the super-pixel scale algorithm (SE-HMRF) for oil spill recognition in SAR images. Super-pixel segmentation of SAR images using simple linear iterative clustering (SLIC) to overcome the effect of speckle noise in SAR images. To improve the accuracy, HMRF is constructed to describe the spatial relationship of the image based on super-pixel segmentation, and transform the problem into an energy function minimization problem by theorems. To overcome the over-segmentation or under-segmentation of oil spill edges by SLIC, the oil spill edge information is introduced into the energy function to constrain the oil spill identification results. To verify the accuracy of this study's algorithm for oil spill identification, Sentinel-1 satellite SAR images are selected for comparison experiments, and the Kappa coefficient and probability Rand index of the oil spill identification results of this study's algorithm reach 0.951 and 0.954, respectively, while the global consistency error is only 0.024, and the results of the qualitative and quantitative evaluations are both better than those of the comparison algorithms, indicating that this study's algorithm can maintain the identification efficiency while obtaining accurate oil spill identification results.