Wavefront restoration based on deep learning is to obtain Zernike coefficients of wavefront aberration directly from the input light intensity image using the trained convolutional neural network (CNN) model. This method has many advantages, such as without iterative calculation, simple and easy to implement, and easy to quickly obtain phase. The training of CNN is carried out by training a large number of light intensity images of distorted far field and their corresponding Zernike wavefront coefficients, automatically extracting the characteristics of light intensity images, and learning the relationship between light intensity and Zernike coefficients. In this paper, a CNN-based wavefront restoration model is established based on the 35-order Zernike-atmospheric turbulence aberration. By analyzing the ability of this method to restore static wavefront distortion, the feasibility and restoring ability of the CNN based wavefront restoration are verified.