The morphology of the auroral oval is an important geophysical parameter that can be used to uncover the solar wind-geomagnetic field interaction process and the intrinsic mechanism. However, it is still a challenging task to automatically obtain auroral poleward and equatorward boundaries completely and accurately. In this paper, a new model based on the deep feature and dual level set method is proposed to extract the auroral oval boundaries in the images acquired by the Ultraviolet Imager (UVI) onboard the Polar spacecraft. With the deep feature extracted by the convolutional neural network (CNN), the corresponding deep feature energy functional is constructed and incorporated into the variational segmentation framework. The dual level set method is implemented to extract the accurate poleward and equatorward boundaries with the gradient descent flow. The experimental results on the test data set demonstrate that this model can extract complete auroral oval contours that are consistent well with expert annotations and owns higher accuracy compared with the previously proposed methods. Besides, the comparison between the extracted auroral boundaries and the precipitating boundaries determined by Defense Meteorological Satellite Program (DMSP) SSJ precipitating particle data validates that the proposed method is trustworthy to capture the global morphology of the auroral ovals.