This paper proposes a neural model for closed-set Chinese word segmentation.The model follows the character-based approach which assigns a class label to each character,indicating its relative po-sition within the word it belongs to.To do so,it first constructs shallow representations of characters by fusing unigram and bigram information in limited context window via an element-wise maximum operator,and then build up deep representations from wider contextual information with a deep convolutional network.Experimental results have shown that our method achieves better closed-set performance compared with several state-of-the-art systems.