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Penicillin fermentation processes have obvious stage characteristics, meanwhile which have great uncertainties due to some reasons of variable operation conditions and complex production environments, this paper aims to establish a finite impulse response (FIR) fusion model under the variational Bayesian (VB) framework for online prediction of penicillin concentration. First, the scheduling variable is selected to divide fermentation stages, then the parameters of each FIR sub-model are identified based on the VB algorithm. Finally, the probability of the sample belonging to each sub-model is calculated according to the stage characteristics, and further applied to fuse sub-model outputs for obtaining the penicillin concentration predictions. The paper uses the actual industrial scale penicillin fermentation data to carry out simulation experiments. The correlation error of the model predicting penicillin concentration is 0.24% which shows that the model has a high degree of fitting, which can provide more accurate prediction of the penicillin concentration and adapt to the actual complex industrial environments.