Click-Through Rate ( CTR) prediction is the foundation of search advertising. Nowadays, lots of researches have been explored to predict CTR, and most of those researches either rely on liner model or employ method of recommen-dation system. However, the relations between different features in CTR predication have not been fully explored in previ-ous works, and the relations between different features also cannot be fully embodied. In this paper, CTR prediction for search advertising based on convolution neural network is proposed, and process of convolution neural network simulating the process of human thought on feature learning is explained. Furthermore, the performance of different features have been analyzed in the task of predicting CTR. Experiments are conducted on the dataset of KDD Cup 2012 Track2 and the pro-posed method achieves 0. 792 5 in AUC, demonstrating the effectiveness of the proposed approach.%广告点击率的预测是搜索广告进行投放的基础。目前已有的工作大多数使用线性模型或基于推荐方法的模型解决点击率预测问题，但这些方法没有对特征之间的关系进行深入的探索，无法完全体现广告点击预测中各个特征之间的关系。本文提出了基于卷积神经网络的搜索广告点击率预测的方法，阐述了卷积神经网络在特征的学习上模拟人的思维过程，并进一步分析了不同特征在广告点击率预测中的作用，在KDD Cup 2012中Track 2数据集上的实验结果验证了本文提出的方法能够提高搜索广告点击率的预测效果，其AUC值达到0.7925。
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