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To overcome the difficulty that crucial biological variables ( such as substrate concentration，biomass concentration，product concentration，etc.) cannot be effectively controlled during the marine microbe fermentation process due to a lack of real-time on-line instrumentation，a soft sensor method is proposed by combining the Kernel Principal Component Analysis ( KPCA) with the Dynamic Fuzzy Neural Network (DFNN).The typical marine microbe fermentation process (the marine protease fermentation process) was taken as an example. Firstly, KPCA was applied to choose the nonlinear principal component of the model input data space. And then its result was taken as input of the DFNN, substrate concentration , biomass concentration and relative enzyme activity were taken as output of the DFNN. Finally, the soft sensor model of biological parameters based on KPCA-DFNN is established in the marine protease fermentation process. Simulation results indicate that the KPCA-DFNN model has a higher accuracy, better tracking performance when compared with DFNN model and the PCA-DFNN model. Therefore, the proposed method can satisfy the requirements of on-line measurement of biological variables in the marine microbe fermentation process.