Super-resolution microscopy techniques invented at the beginning of the 21 ^st century provide unprecedented access to life science researches owing to its impressive ability of studying subcellular structures at the micrometer and nanometer scales. However, these techniques often require high cost of time and money. Recently, many researchers work on super-resolution image reconstruction algorithms based on deep learning. Herein, we obtained the super-resolution images of cell microtubule cytoskeletons by the self-built stochastic optical reconstruction microscopy (STORM), and then the bilinear interpolation down-sampling method was used to obtain the low-resolution input atlas. The traditional cubic spline interpolation method and the enhanced depth super-resolution neural network were used for learning and training to realize the super-resolution reconstruction of the low-resolution image. Results show that the effects of all kinds of down-sampling images reconstructed by deep learning are better than those obtained by traditional interpolation method; the super-resolution images of microtubule skeletons obtained by double down-sampling and experiments are comparable in subjective and objective evaluation indexes. Based on the enhanced depth super-resolution neural network, the super-resolution reconstruction of cytoskeleton images is expected to provide a simple, effective, and cost-effective imaging method, which can be applied to the rapid prediction of cytoskeleton super-microstructures.