传统机器学习算法的预测精度往往依赖于具体的问题，集成学习通过综合若干基分类器的预测结果，实现了分类效果的显著提升。对集成学习的思想进行了简单地介绍，阐述了Stacking集成相对于传统经典集成算法的优势。并基于Stacking集成框架，利用UCI的信用评估数据集，构建两层分类器学习模型对个人信用进行评估。实证分析的结果表明，相对于单一的机器学习方法，以及对这些单一机器学习方法的结果进行简单的平均集成，两层分类器的Stacking集成学习有着更好的预测效果。 The prediction accuracy of traditional machine learning methods often depends on the specific problems. Ensemble learning achieves significant improvement in classification performance by combining several of base classifiers. This paper briefly introduces the basic idea of ensemble learning, discusses advantages of Stacking to the traditional classical ensemble algorithms. Based on the Stacking framework，we build two-layer classification model to evaluate the personal credit using the UCI datasets. The results of the empirical analysis show that, compared with the single machine learning method and simple average ensemble, Stacking with two-layer classifier has a better prediction effect.