Traditional Chinese word segmentation (CWS) methods are based on supervised machine learning such as Condtional Random Fields(CRFs), Maximum Entropy(ME), whose features are mostly manual features. These manual features are often derived from local contexts. Currently, most state-of-art methods for Chinese word segmentation are based on neural networks. However these neural networks rarely introduct the user dictionary. We propose a LSTMbased Chinese word segmentation which can take advantage of the user dictionary. The experiments show that our model performs better than a popular segment tool in electricity domain. It is noticed that it achieves a better performance when transfered to a new domain using the user dictionary.