To tackle the problems of the underutilization of context, the sparseness of data and the dependence on human-designed features in existing Chinese microblog sentiment classification methods, a Chinese microblog sentiment classification method based on convolutional neural network is proposed. Firstly, microblog messages are extended using the interaction context, and then they are initialized with dense vectors in the low-dimension space. Secondly, a convolutional neural network model is constructed for extracting and combining features. Finally, the sentiment of each microblog message is estimated by softmax function. Experimental results show that compared with baselines, the proposed method obtains higher accuracies and F1 values.%针对现有中文微博观点分类方法对上下文利用不足、据表示稀疏和特征依赖于人工设计等问题，提出基于卷积神经网络的中文微博观点分类方法。首先利用交互上下文扩充不同主题下的微博内容，使用低维密集向量初始化微博文本。然后构造卷积神经网络模型，实现特征抽取和组合。最后基于softmax分类函估计中文微博观点类别。实验表明，相比基准方法，文中方法在精度和F1值上的效果好。
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