Abstract: Based on the hourly observation data of Guiyang National Meteorological Station from 2015 to 2018, 11 meteorological factors affecting fog and dense fog were summarized. Based on the analysis of the relationship between influencing factors and visibility, 80% of the sample data were randomly selected as the training set, and the remaining 20% of the sample data were used as the test set of the prediction model. The prediction models of fog and dense fog in Guiyang station were constructed by using C5.0, CART and neural network algorithms in machine learning, and the application effects of each prediction model were tested and evaluated. The results show that the fog and dense fog prediction based on machine learning has a good business application prospect, and the test accuracy of the three algorithms is above 90%. Among them, the CART algorithm has the best prediction effect on the fog at Guiyang station, and the C5.0 algorithm and the multi-layer perceptron algorithm in the neural network have the best prediction effect on the dense fog at Guiyang station.