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The rain and fog weather data in the highway area presents multi-source and heterogeneous characteristics, and it is very difficult to make short-term predictions accurately. Aiming at this problem, a heterogeneous data fusion model is constructed based on the joint probability of data obtained by the joint probability method, and the fusion model is combined with the Kalman filter method to establish a collaborative fusion filter for multi-source heterogeneous meteorological data model to fuse meteorological data. On this basis, using the Bayesian Maximum Entropy (BME) method, combined with the rain and fog empirical theory, fusion filtered data and original rain and fog data, a short-term accurate prediction of the rain and fog weather conditions of the target section of the highway is realized. Experimental results show that this method can provide accurate and stable expressway short-term rain and fog weather forecast results, which is of great significance for reducing traffic accidents and reasonably implementing traffic control.