Flyrock is one of the most hazardous events in open-pit mine blasting operations.At present,the flying distance of flyrock is mainly predicted by several empirical formulas.However,due to the great difference between different engineering,blasting types and parameters,the accuracy of this kind of empirical model is not high.Besides,the unknown weight relationship of various parameters is the main reason for the inaccuracy of the empirical model.To solve this problem,artificial intelligence and other new methods can be used to predict the flying distance of flyrock.During the blasting operation of Yingtaojing limestone mine in Qingzhen city,Guiyang,flying stones were scattered far beyond expectations,resulting in the damage of surrounding protected buildings.In this paper,an attempt is made to predict and control flyrock in blasting operation of this mine using artificial neural network method.Based on 122 blasting test data,7 different neural networks are trained to model.Among them,a three-layer feedforward back-propagation neural network has the lowest root mean square error,which was composed of 16 hidden neurons,including 9 input parameters and 1 output parameter.By comparison,the average relative error between the predicted results and the measured data is within 3.31%,which has guiding significance.The sensitivity analysis of the model shows that the parameters that have the greatest influence on the distance of blasting flyrock are explosiveness index,delay time,charge per delay,hole diameter,hole depth and karst cave.