Abstract: Unlike general sentiment analysis, aspect-based sentiment classification aims to infer the sentiment polarity of a sentence depending not only on the context but also on the aspect. For example, in sentence “The food was very good, but the service at that restaurant was dreadful”, for aspect “food”, the sentiment polarity is positive while the sentiment polarity of aspect “service” is negative. Even in the same sentence, sentiment polarity could be absolutely opposite when focusing on different aspects, so we need to infer the sentiment polarities of different aspects correctly. The attention mechanism is a good way for aspect-based sentiment classification. In current research, however, the attention mechanism is more combined with RNN or LSTM networks. Such neural network-based architectures generally rely on complex structures and cannot parallelize over the words of a sentence. To address the above problems, this paper proposes a multi-attention convolutional neural networks (MATT-CNN) for aspect-based sentiment classification. This approach can capture deeper level sentiment information and distinguish sentiment polarity of different aspects explicitly through a multi-attention mechanism without using any external parsing results. Experiments on the SemEval2014 and Automotive-domain datasets show that, our approach achieves better performance than traditional CNN, attention-based CNN and attention-based LSTM.