为了使得基坑变形预测在“少样本”“贫信息”的情况下依然能够得出精度较高的结果，在传统的灰色GM(1,1)模型和BP神经网络模型的基础上，进行了灰色BP神经网络组合模型的研究。通过总结2传统模型的原理和算法，归纳各自的优缺点，分析2模型在本质原理上的关系，提出了构建组合模型的方法。利用广州市轨道交通三号线燕塘站的监测数据，对灰色GM(1,1)模型、BP神经网络模型和灰色BP神经网络组合模型分别进行了检验，肯定了组合模型的优越性。

In order to get accurate results of the foundation deformation prediction under the conditions of "limited sample" and "limited information", the research of Gray BP neural network combined model is proposed, based on the traditional models including GM (1,1) model and BP neural network model. By summarizing the two traditional models' principles and algorithms, concluding their respective advantages and disadvantages and analyzing the nature of the relationship between the models, the way of structuring the combined model is given out. Finally, the test of Gray GM (1, 1) model, BP neural network model, Gray BP neural network combined model, by using of the monitoring data of theYantang station on No.3 Line of GuangzhouMetro, confirms the superiority of the combined model.