Most automatic weather station use optical devices to sample visibility. There are two problems with this. One problem is inaccurate sample date caused by weather factors such as rain, snow or dust in which the lens of those optimal devices might be soiled, and thus the collected data is not accurate. Another problem is data missing due to aging equipment, equipment maintenance, etc. To get rid of those wrong sample and provide complete data for meteorological prediction, this paper proposes a SVM-BP neural network based data quality control method for weather visibility according to those historical data from the Anhui Meteorological Bureau. Firstly, the grey correlation analysis method is used to screen out other meteorological elements closely related to visibility. Secondly, the optimal weight combination is used to combine the support vector machine and the BP neural network. The combined estimation of the visibility of different terrains is then carried out and compared with a single estimation method. The result shows that the combined estimation method has a smaller mean error, higher overall accuracy, and provide an effective theoretical basis for short-term weather forecasting, live analysis, scientific research and public service.