In order to predict product quality and optimize production process, the product quality model needs to be built. According to the fact that the common methods always cost long training time and can not realize real-time update, an online product quality model based on the online support vector regression is here proposed. The real field data of zinc coating weights from strip hot-dip galvanizing are used for validation. The results show that the models based on the online support vector regression have a higher prediction precision and shorter training time than traditional support vector regression, which is convenient to complete the real-time update. The zinc coating weights forecasting model based on the online support vector regression for multi-group data has an average of the relative prediction error of 4.35%, thus for the model will be used as an analysis tools for the quality control.