地表下沉系数是地表沉陷预计中的重要参数，其取值的精度会对沉陷预计结果产生直接的影响，由于煤矿开采地表下沉系数影响因素众多且因素间存在着不确定性和非线性等复杂关系，从而导致地表下沉系数预测工作极为困难。为解决地表下沉系数难以准确预测的问题并提高预测精度，根据国内35个矿区的实测地表移动观测站数据，构建地表下沉系数预测模型。选取开采厚度、煤层倾角、平均釆深、走向宽深比、倾向宽深比、推进速度、松散层厚度和覆岩平均坚固系数等8个影响因素，采用灰色关联度分析和主成分分析相结合的方法求取地表下沉系数影响因素的组合权重，根据组合权重对地表移动观测站数据中的地表下沉系数影响因素进行排序，获得影响地表下沉系数的主要影响因素，并将主要影响因素作为输入，地表下沉系数作为输入参数，进而提出一种地表下沉系数预测分析的BP神经网络模型。结果表明：松散层厚度、推进速度、平均采深和倾向宽深比的组合权重更大，是地表下沉系数的主要影响因素；由地表下沉系数主要影响因素建立的地表下沉系数BP神经网络预测模型的预测精度高，其绝对误差最小值为3.954%，最大值仅为-6.918%，平均相对误差可以达到7.179%，与实测值极其接近。模型预测精度能够满足基本的工程需要，是地表下沉系数准确预测的一种可行方法。

The surface subsidence coefficient is an important parameter in the prediction of surface subsidence. The accuracy of its value will have a direct impact on the prediction results of subsidence. Because there are many factors affecting the surface subsidence coefficient of coal mining，and there are complex relationships such as uncertainty and nonlinearity among the factors，the prediction of surface subsidence coefficient is very difficult. In order to solve the problem that it is difficult to accurately predict the surface subsidence coefficient and improve the prediction accuracy，a prediction model of surface subsidence coefficient is established based on the measured surface movement observation data of 35 mining areas in China. The combination weight of influencing factors of surface subsidence coefficient is obtained by combining grey correlation analysis and principal component analysis. Eight influencing factors such as mining thickness，coal seam dip angle，average mining depth，strike width depth ratio，dip width depth ratio，advancing speed，loose layer thickness and average firmness coefficient of overburden are selected. According to the combination weight，the influencing factors of surface subsidence coefficient in the data of surface movement observation station are sorted，and the main influencing factors of surface subsidence coefficient are obtained，and the main influencing factors are taken as the input and the surface subsidence coefficient is taken as the input parameter. Then a BP neural network model for the prediction and analysis of surface subsidence coefficient is proposed. The results show that:the combination weight of loose layer thickness，advancing speed，average mining depth and dip width depth ratio is larger，which is the main influencing factor of surface subsidence coefficient; the BP neural network prediction model of surface subsidence coefficient established by the main influencing factors of surface subsidence coefficient has high prediction accuracy，the minimum absolute error is 3.954%，the maximum value is only - 6.918%，and the average relative error can reach 7.179%，which is very close to the measured value. The prediction accuracy of the model can meet the basic engineering needs，and it is a feasible method to accurately predict the surface subsidence coefficient.