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Sentence classification is one of the foundational tasks in spoken language understanding (SLU) and natural language processing (NLP). In this paper we propose a novel convolutional neural network (CNN) with attention mechanism to improve the performance of sentence classification. In traditional CNN, it is not easy to encode long term contextual information and correlation between non-consecutive words effectively. In contrast, our attention-based CNN is able to capture these kinds of information for each word without any external features. We conducted experiments on various public and inhouse datasets. The experimental results demonstrate that our proposed model significantly outperforms the traditional CNN model and achieves competitive performance with the ones that exploit rich syntactic features.