为了分析边坡的稳定性，利用协调粒子群算法和BP网络建立了边坡稳定性CPSO-BP预测模型。BP网络能够很好地描述边坡稳定性与其影响因素之间复杂的非线性关系，将内摩擦角、边坡角、岩石重度、边坡高度、黏聚力、孔隙压力比6个主要影响因素作为网络的输入，将边坡稳定性系数作为网络的输出。为避免BP网络陷入局部最优，利用协调粒子群算法的全局优化能力确定BP网络的连接权值和阀值，使BP网络的优势得到分发挥，达到提高模型预测精度目的。实例表明CPSO-BP模型有更好地预测精度以及将其应用于边坡稳定性预测是可行的。

In order to analyze the stability of slopes, by using coordinated particle swarm optimization(CPSO) and BP neural network, a CPSO-BP model is built to predict slope stability. BP neural network can describe the complex nonlinear relationship between the slope stability and its influential factors; six main influential factors, i.e. internal friction angle, slope angle, unit weight of rock, slope height, cohesion and pore pressure ratio, as a network input, the network output is the slope stability factor. BP neural network is so easy to fall into local optimum that take advantage of coordinated particle swarm optimization algorithm the ability of global to determine BP neural network connection weights and thresholds, let the advantages of BP neural network can fully play. Frinally we will approach to the aim that improving the accuracy of model predictions. Case study shows that the CPSO-BP model has better prediction accuracy and it is feasible to predict the slope stability.