The ability of image features expression with the dictionary designed by artificial shallow features is limited in dictionary based single image super-resolution reconstruction algorithm. For the reason, an image super resolution reconstruction algorithm based on deep learning and feature dictionary is proposed. Firstly, deep-level feature learning is carried out in high and low resolution training sample images by using deep network. Secondly, the feature dictionary is trained with the combination of sparse coding under the sparse dictionary super resolution frame. Finally, a low resolution image is put in and the super resolution reconstruction is realized by using the dictionary. Theoretical analysis shows that the combination of image deep-level feature extraction and dictionary training by using deep network is more beneficial to high frequency information supplement for low resolution image. Experimental results show that compared with bicubic interpolation and other general artificial feature dictionary based super resolution reconstruction algorithms, the proposed algorithm has better subjective visual and objective evaluation indices.