Abstract: The tangent of major influence angle (tanβ) is an important parameter for mining subsidence prediction，and it plays a key role to determine the subsidence basin boundary.In order to calculate the tanβ value quickly and accurately to improve the accuracy of mining subsidence prediction effectively.Firstly,the geological and mining factors that are affecting the value change of tanβ are discussed in detail,the five basic variables (mining thickness,dip angle of coal seam,mining depth,working face slanting length,lithology influence coefficient) are determined;then,the basic principles and basic implementation process of random forest algorithm (RF) are analyzed in depth;finally,the random forest regression model is established to calculate the tanβ value,the actual measured data of the typical surface movement observation stations that are located in the part of the main mining areas in China are regarded as the training and test samples of the regression model established in this paper.The test samples are used to conduct inspection and analysis of the random forest regression model after training,the results show that:①the minimum relative error and maximum relation error between the tanβ calculated by the regression model established in this paper and the actual measured data are 0.381% and 2.563% respectively.②the regression model established in this paper has stronger generalization ability,it has the characteristics of fast calculation speed and high accuracy in the calculation process of tanβ,besides that,it also has a good application prospect in the practical engineering,it can provide reference for calculating the tanβ value with high accuracy.

Key words: Random forest algorithm, Tangent of major influence angle, Mining subsidence, Subsidence basin, Regression model