The invention relates to a g lass furnace temperature forecast method based on a learning machine related to Gaussian mixture distribution, and belongs to the automatic control field, the information technology field, and the advanced manufacturing field. Modeling problems of glass furnace temperature forecast such as complicated glass furnace internal reaction process, complicated asymmetric noises of data, and input variables including time series variables, the glass furnace temperature forecast method based on the learning machine related to robustness in the Gaussian mixture distribution is provided. A kernel function regression model is used as a forecast model structure, and non-zero mean value Gaussian mixture distribution is used as probability density distribution of forecast model residual terms, and the time series variables are parallely arranged, and are used as the input variables of the models, and then a Bayesian inference method is used to acquire approximate posterior probability distribution of model structure parameters, and therefore the structure parameters of the forecast model is acquired. The glass furnace temperature forecast method is effectively used for the forecast of the glass furnace pool bottom temperature, and therefore a glass furnace control and operation optimization effect is improved.