To solve the problems of image detail loss and unclear texture caused by interference factors such as noise， imaging technology and imaging principles in the medical Magnetic Resonance Imaging （MRI） process， a multi-receptive field generative adversarial network for medical MRI image super-resolution reconstruction was proposed. First， the multi-receptive field feature extraction block was used to obtain the global feature information of the image under different receptive fields. In order to avoid the loss of detailed texture due to too small or too large receptive fields， each set of features was divided into two groups， and one of which was used to feedback global feature information under different scales of receptive fields， and the other group was used to enrich the local detailed texture information of the next set of features； then， the multi-receptive field feature extraction block was used to construct feature fusion group， and spatial attention module was added to each feature fusion group to adequately obtain the spatial feature information of the image， reducing the loss of shallow and local features in the network， and achieving a more realistic degree in the details of the image. Secondly， the gradient map of the low-resolution image was converted into the gradient map of the high-resolution image to assist the reconstruction of the super-resolution image. Finally， the restored gradient map was integrated into the super-resolution branch to provide structural prior information for super-resolution reconstruction， which was helpful to generate high quality super-resolution images. The experimental results show that compared with the Structure-Preserving Super-Resolution with gradient guidance （SPSR） algorithm， the proposed algorithm improves the Peak Signal-to-Noise Ratio （PSNR） by 4.8%， 2.7% and 3.5% at ×2， ×3 and ×4 scales， respectively， and the reconstructed medical MRI images have richer texture details and more realistic visual effects.