Czochralski silicon single crystal growth is a dynamic time-varying process with multi-field and multi-phase coupling, complex physical changes, nonlinearity and large hysteresis. However, mechanism models based on a large number of assumptions are difficult to apply in practice. Therefore, this article is based on long-term and massive crystal growth data from the CL120-97 single crystal furnace of crystal pulling workshop, which ignores the complex crystal growth environment in the furnace, analyzes the correlation of the crystal pulling parameters that affect the crystal diameter, mines the regular information contained in the data, and further builds a crystal diameter prediction model based on the BP neural network. Aiming at the problem that the existing BP neural network model is easy to fall into the local minimum, the genetic algorithm is used to optimize the weight and threshold of the BP neural network to improve the accuracy of the crystal diameter prediction of the algorithm. The model prediction results are verified by actual crystal pulling data. The results show that the average relative percentage error of prediction is 0.095 71% for diameter prediction of 8 groups of randomly selected crystal pulling data. It is proved that the model is feasible for the prediction of crystal diameter in the equal-diameter stage.