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In view of problems of low test precision and slow training speed in rotary kiln calcination temperature forecasting model with traditional algorithm, we proposed a forecasting method of rotary kiln calcination temperature based on improved extreme learning machine (ELM). A variable of the input weights’ transform coefficient of ELM was defined in the method, and golden section method was used to search the best value of the transform coefficient within a given range. The method improved the way to determine the network parameter of ELM, compensated the defects of input weights’ random generation and no adjustment, on the premise of ELM’s training speed, it increased forecasting accuracy and reduced the randomness of the model. Simulation result showed that the improved ELM model was superior to the original one, and had high accuracy of prediction and fast training speed, it can meet the need of rotary kiln in the bad working environments.