The output speed of ESPCP system is caused by the coupling of multi-factors and it is a typical nonlinear optimization problem. The key factor affecting life of progressing cavity pumping system is the rotor speed. The associated constraints depends on the physical and chemical conditions including pressure difference between pressure difference of the pump two ends, oil viscosity, temperature and wear clearance, etc. Taken these parameters as original inputs, the nonlinear mapping relationship between rotor’s rotational speed and its affecting factors are determined by means of artificial neural network, and establish the output speed prediction model. After the simulation through samples study and by combining field data, it shows that the predicting results is basically in accordance with the observation data, and acquire satisfactory results. The research results show that the artificial neural network is an ideal method of ESPCP system speed optimization.