Abstract: As one of the important parameters of mining subsidence prediction，the horizontal displacement factor b plays a decisive role in determining the scope of the surface effect of mining subsidence.In order to calculate the b value effectively and accurately,and thus improve the accuracy of mining subsidence prediction with high efficiency.Firstly,the main characteristics of R language and the basic principle of random forest（RF) algorithm and its implementation process are analyzed in depth;then,the geological and mining factors that affecting the change of b value are discussed,the five basic variables such as mining thickness,dip angle of coal seams,mining depth,slanting length of working face,overlying rock evaluation coefficient are determined;finally,the RF regression model is established based on R language,and it is used to prediction the b value.The traning samples and test samples of the RF regression model is the measured data obtained from the typical surface movement observatorys located in the main mining area in our country,the experimental results show that maximum relative error of the measured data and the prediction results of the RF regression model based on R language is only 4.559%;the prediction accuracy and stability of the RF regression model are superior to the BP neural network and support vector machine（SVM),besides that,the generalization ability of RF regression model is strong,it can meet the actual engineering requirements and provide a effective method for predicting the b value.

Key words: Mining subsidence, Random forest, Horizontal displacement factor, R language, Geological and mining factors