Long-Term and Short-Term Memory (LSTM) network is a time recursive neural network, which is suitable for predicting events with relatively long delay in time series. In this study, a new fog proximity prediction framework based on LSTM network is constructed, which can transform meteorological observation data into time series data and model them based on time series data. In order to validate the proposed model effectively, this study transforms the surface meteorological data of 81 national stations in Anhui Province from October 2015 to June 2017 into sequence data and constructs a validation data set. Based on this data set, the future 1-4 hourly fog forecasting experiments are carried out. The experimental results show that the proposed model's TS-Scores are 61%, 55%, 36%, and 31%, respectively, which are obviously better than CNN and those of traditional machine learning algorithms such as SVM and KNN. It is an effective method for fog prediction.