Turbulence image restoration is a hot topic in the field of meteorology, but there are few studies on the classification of turbulent disturbance intensity images. Aerial images of different scenarios are processed for atmospheric turbulence. 2000 corresponding turbulent interference images are generated by adjusting the intensity of turbulence degradation value, and then they are sent into the convolutional neural network after the image preprocessing in turbulent degradation intensity classification, finally by optimizing the structures, convolutional neural network model of the activation function and the adjustment of vector for further improving classification accuracy. Experiments show that the classification accuracy of turbulent degradation images which have different interference intensities by convolutional neural network is about 80%, which demonstrates that this method is of guiding significance for the restoration of atmospheric turbulent degradation images.