The aim of this study is to analyze the application of early rehabilitation nursing in nursing intervention of neurological impairment among patients with acute ischemic stroke. 116 patients with acute ischemic stroke were selected as the research subjects in this paper. The patients were divided into 58 experimental (early rehabilitation care) and 58 control (routine rehabilitation care) groups according to the difference of care protocols, all of which were performed magnetic resonance imaging on. An image resolution reconstruction algorithm on the basis of deep convolutional neural network is proposed for MRI image processing. The results show that peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the included algorithm were remarkably greater than those of compressed sensing (CS) algorithm and nonlocal similarity and block low rank prior-based NSBL algorithm. Running time was shorter than that of the latter two algorithms P < 0.05 . The neurological impairment scores of patients in the experimental group 3 and 5 weeks after treatment were obviously lower than those of patients in the control group P < 0.05 . The Barthel indexes of patients in the experimental group 3 and 5 weeks after treatment were obviously higher than those of patients in the control group P < 0.05 . FugI-Meyer assessment (FMA) and Disability of Arm-Shoulder-Hand (DASH) scores of patients in the experimental group 3 and 5 weeks after treatment were obviously lower than those of patients in control group P < 0.05 . The results show that the deep learning algorithm for MRI image processing performance is better than the traditional algorithm. It not only improves the image quality but also improves the processing efficiency. Early rehabilitation nursing and routine rehabilitation nursing can effectively improve the neurological deficit symptoms, limb motor function, and daily living ability of patients with acute ischemic stroke, and the effect of early rehabilitation nursing is the best.