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An accurate and effective fermentation process model can not only quantitatively reveal the correlation between process information and realize the prediction of variables that are difficult to monitor in real time, but also is a prerequisite for further control and optimization. The data-driven modeling method has been widely researched and applied. However, it only considers the nonlinear characteristics of the fermentation process and the characteristics of the data with multiple sampling rates, and ignores the influence of measurement noise in the process data on the model. For this reason, a regression modeling method for fermentation process based on stacked denoising autoencoder is proposed. This method not only has strong nonlinear fitting ability, but also semi-supervised learning strategy can fully mine all data information in the fermentation process. At the same time, robust features can be extracted from the noisy process data, so that the model has certain noise adaptability. The results of penicillin simulation and comparison experiments show that the prediction performance of this model is better.