Accurately identifying the visibility levels of foggy images is of great significance for the early warning and prevention of fog disasters. Aiming at the problems of large training parameters and low recognition accuracy in current fog disaster warning models, a foggy image recognition model based on the improved VGG16 was proposed. This model took VGG16 as the basic architecture and incorporates the enhanced convolutional block attention module (ECBAM) and multi-loss functions to enhance model precision. Additionally, it replaced the three-layer fully connected layer with a two-layer structure and adds group normalization (GN) layers to accelerate model convergence. The optimized model reduced the number of parameters while improving training speed and recognition accuracy. Experimental results show these as follows. The improved model reduces the number of parameters by 52.22%, achieves a recognition accuracy of 88.96%, which is a 5.34% improvement over the original VGG16 model, and demonstrates higher recognition accuracy than comparative models such as AlexNet, VGG16, ResNet-50, and GoogLeNet. The recognition accuracies of the improved model for fog-free, light fog, dense fog, and thick fog images are 92.96%, 89.00%, 83.04%, and 81.76%, respectively, and the accuracy for extremely dense fog images is 73.75%. While the model meets daily operational needs, there is still room for optimization in recognizing extremely dense fog; Application tests using foggy videos from non-training periods show that the model can accurately identify fog disasters, with results consistent with those observed by visibility detection instruments. The proposed method can be applied to practical meteorological operations and holds high business application value.