Abstract Aspect-based sentiment classification aims to predict the sentiment polarity of an aspect term in a sentence instead of the sentiment polarity of the entire sentence. Neural networks have been used for this task, and most existing methods have adopted sequence models, which require more training time than other models. When an aspect term comprises several words, most methods involve a coarse-level attention mechanism to model the aspect, and this may result in information loss. In this paper, we propose a multi-attention network (MAN) to address the above problems. The proposed model uses intra- and inter-level attention mechanisms. In the former, the MAN employs a transformer encoder instead of a sequence model to reduce training time. The transformer encoder encodes the input sentence in parallel and preserves long-distance sentiment relations. In the latter, the MAN uses a global and a local attention module to capture differently grained interactive information between aspect and context. The global attention module focuses on the entire relation, whereas the local attention module considers interactions at word level; this was often neglected in previous studies. Experiments demonstrate that the proposed model achieves superior results when compared to the baseline models.