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In view of the current microbial fermentation process exists because of biological sensors do not have enough accuracy and sensitivity, the experiment of the microbial and biochemical indexes such as difficult to real-time monitor and control product concentration such as faults, puts forward using quantum particle swarm optimization (QPSO) algorithm to optimize the least squares support vector machine (LSSVM) parameters of QPSO - new LSSVM hybrid modeling method, and used in the fermentation process modeling polymyxin; At the same time, based on this model, using QPSO algorithm to control the pH and dissolved oxygen concentration Do trajectory optimization research. First of all, the use of LSSVM for fermentation process modeling, and the important parameters in the process of using QPSO on LSSVM modeling optimization adjustment, formed QPSO LSSVM hybrid modeling and optimization control method. Model of the simulation results show that the approach can obtain better prediction result, the optimal pH and the Do concentration control trajectory can improve the final product concentration. The method used in the fermentation process modeling and optimal control of important parameters are feasible and effective.