The growth process of Czochralski silicon is related to multi-field and multi-phase coupling and complex physical and chemical changes. The fluctuations in process parameters are an important cause of uneven crystal diameter. This is significant research on how to achieve the control of process parameters to obtain an ideal and uniform crystal diameter. In this paper, after analyzing the instability and control effect degradation of the existing control method, a model-free adaptive control model based on Bayesian parameter optimization is presented to control the crystal diameter of the monocrystalline silicon growth process. Firstly, a model-free adaptive control model was established with crucible rising speed and heater power as control input parameters and crystal diameter as output parameters, and the stability of the algorithm was analyzed. Secondly, the control model was simulated and it is found that different super-parameter settings in the Czochralski silicon diameter control model will affect the number of iterations and the control effect. Finally, the value range of the hyper-parameter was optimized by Bayesian and the final simulation experiment was carried out. The results show that the model-free adaptive control optimized by the hyper-parameters is faster in calculation, less in iteration times, and the growth process parameters are controlled within the actual production requirements. Therefore, the model-free adaptive control based on Bayesian parameter optimization can effectively control the diameter of monocrystalline silicon, which has a practical application prospect combined with engineering background.