Random domain names refer to the names generated by domain generation algorithms, which are widely used by the malware of the computer network system. The detection of random domain names is the basic work of the traffic filtering operation of the domain name system. The traditional method of detecting random domain name is not ideal and the precision and recall are low which will lead to erroneous judgement in attack traffic filtering. In this paper, the random names detection model are built based on recurrent neural network with gated recurrent unit. In this model, domain names are converted to vectors at first, then GRU are adopted to learn features automatically and which will be taken by the neural network to compute the class scores. Compared to traditional methods, this method is able to extract features without human help and which will reduce the time cost of feature extraction. This method performs better in the experiments of the algorithm generated data and the real world data than traditional models.