中文摘要：

如何能比较准确地预测滑坡的发生，已成为各矿山开采过程中的难题之一。对人工神经网络及BP网络模型作了简要的介绍，分析BP网络的结构特点、参数选择、数据收集与处理、构造网络模型等问题之后，以中核金安铀矿的边坡稳定状况为学习训练样本及预测样本，建立了预报模型。讨论了基于BP神经网络技术的边坡岩体稳定性分析方法及其有效性。实例计算表明，通过样本的训练检验，利用人工神经网络方法对边坡稳定性的预测取得了比较满意的效果，为今后此类边坡稳定性的评价提供了可借鉴的方法。为神经网络在矿山边坡稳定性的应用提供了可行性。

英文摘要：

How to accurately forecast the landslide has become one of the challenges of mining process. The paper describes briefly neural network and BP network model, and analyzes BP network＇s structural characteristics, parameter selection, data collection and processing, and construction of network model. A forecast model is established with the slope stability state in CNNC Jin＇an Uranium Mine as the learning and training sample and the forecast sample. The stability analysis method based on BP neural network technology and its effectiveness are discussed. The real case computation shows that the forecast of slope stability by neural network method has achieved satisfactory results, providing a reference to the assessment of the similar slopes and the feasibility of applying the neural network in mine slope analysis assessment.