Chinese named entities occur frequently in formal and informal environments. Various approaches have been formalized the problem as a sequence labelling task and utilize a character-based methodology, in which character is treated as the basic classification unit. One of the main drawbacks of these methods is that some of the generated abbreviations may not follow the conventional wisdom of Chinese. To address this problem, we propose a novel neural network architecture to perform task. It combines recurrent neural network (RNN) with an architecture determining whether a given sequence of characters can be a word or not. For demonstrating the effectiveness of the proposed method, we evaluate it on Chinese named entity generation and opinion target extraction tasks. Experimental results show that the proposed method can achieve better performance than state-of-the-art methods.