Social bots are regarded as the most common kind of malwares in social platform. They can produce fake messages, spread rumours, and even manipulate public opinions. Recently, massive social bots are created and widely spread in social platform, they bring negative effects to public and netizen security. Bot detection aims to distinguish bots from human and it catches more and more attentions in recent years. In this paper, we propose a behavior enhanced deep model (BeDM) for bot detection. The proposed model regards user content as temporal text data instead of plain text to extract latent temporal patterns. Moreover, BeDM fuses content information and behavior information using deep learning method. To the best of our knowledge, this is the first trial that applies deep neural network in bot detection. Experiments on real world dataset collected from Twitter also demonstrate the effectiveness of our proposed model.