In this paper, a metamaterial Fano resonance design method based on deep learning is proposed to obtain high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude, and spectral position.The deep neural network is used to establish the mapping between the structural parameters and the transmission spectrum curve. In the design, the forward network is used to predict the transmission spectrum, and the inverse network is used to achieve the on-demand design of high Q resonance. The low mean square error ( MSE ) is achieved in the design process, and the mean square error of the training set is 0.007. The results indicate that compared with the traditional design process, using deep learning to guide the design can achieve faster, more accurate, and more convenient purposes. The design of Fano resonance can also be extended to the automatic inverse design of other types of metamaterials, significantly improving the feasibility of more complex metamaterial designs.