The design parameter selection of model predictive control significantly affects the performance of the controlled system. The current mainstream parameter tuning methods based on expert experience have the disadvantages of poor controller robustness and high calculation cost. To solve the above problems， this paper proposes a parameter tuning algorithm based on Fuzzy C-means-Extreme Learning Machine-Bare Bones Particle Swarm （FCM-ELM-BBPS）. Firstly， Fuzzy C-means （FCM） clustering is used to preprocess the data， and the complex data of the controlled system is clustered according to its own characteristics， so as to reduce the training error of the neural network and improve the prediction accuracy. Secondly， for each kind of characteristic data， the Extreme Learning Machine （ELM） was used to establish the mapping relationship model between predictive control parameters and performance indices， and the parameter tuning rules were further obtained. Then the Bare Bones Particle Swarm （BBPS） optimization algorithm is used to tune the predictive control parameters. The Gaussian distribution is adopted to update the particle position， which accelerats the convergence of the objective function and effectively reducs the parameter optimization time. Finally， simulation and experiment of the water tank system are carried out respectively to prove the effectiveness of the proposed algorithm. Experimental results show that， compared with existing methods， the proposed algorithm has more advantages， in which the tuning time is reduced by 34.84%， and the time domain performance indices such as the adjustment time are improved by 43.98%.