Taking VGG16 as the benchmark model, integrating batch normalization, global average pooling and joint loss function, this paper proposed a highway fog visibility classification method based on the convolutional neural network.The experimental results show that the average recognition accuracy of the improved neural network model is 83.9%, which has higher accuracy and better convergence than other models.After the model is encapsulated into the business system for operational verification, the average recognition accuracy can reach 84.9%, and the recognition performance in the daytime is better than that at night.A dynamic generation and elimination process of agglomerate fog in Beijing-Shanghai Expressway on April 4, 2019 was monitored by the business system.The agglomerate fog process has the characteristics of fast movement, small range and short survival time.The application of the system can provide technical support for the traffic management department to deal with the intelligent management and control and decision-making scheduling when the fog occurs.