Abstract: This paper innovatively proposes the use of BP neural network to study the rapid estimation model of equipment price. Taking the belt conveyor as an example, based on the analysis of the main influencing indicators of the belt conveyor price, the BP neural network is applied to the establishment of the rapid estimation model of the belt conveyor. Based on the purchase price of the general contracting belt conveyor of Z design institute, the BP neural network equipment price rapid estimation model is tested. The research shows that the model can greatly improve the efficiency of equipment price quotation without losing accuracy, especially for the solution of multi-parameter and complex general equipment quick quotation problem with long inquiry period. It can save the equipment quotation time from several days to several minutes. By establishing the quick quotation model of equipment price, it can get rid of the influence of subjective and objective factors such as too few inquiry manufacturers, lack of contrast or intentional deviation of quotation manufacturers ' quotation from normal price on the pricing accuracy of equipment price in the process of equipment price inquiry.