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For Francis grain drying tower production process, complex physical and chemical changes, temperature changes of the non-linearity and hysteresis difficult to accurately detect the problem, a temperature forecasting method based on information entropy is proposed. First, support vector machine and gray prediction independent establishment of the first spin-down segment temperature model; using a weighting system integration, information entropy algorithm to optimize the weighting factor to enhance the precision of the model, and the actual operating results show that the value of error of dried grain from ±24.7% to reduced to 8.5%, which verify the validity of the method.