Automatic word segmentation is a fundamental technology of natural language processing. Aiming at the problem of complex feature template and large search space in the traditional Thai word segmentation method, this paper proposes a context character feature based neural network model for Thai word segmentation. The proposed model uses the word distribution table to train the word representation vector, and utilizes a multi-layer neural network classifier for Thai word segmentation. Experimental results on InterBEST 2009 Thai word evaluation corpus show that, compared with the conditional random field model, the Character-Cluster Hybrid segmentation model, and the GLR and N-gram segmentation model, our proposal achieves better performance. Word segmentation accuracy, recall ratio and F value reach 97.27%, 99.26% and 98.26%, respectively. Our model improves the segmentation speed by 112.78% in comparison to the conditional random field model.