Named entity recognition is a basic task of natural language processing. Traditional recognition methods often require external knowledge and manual screening features, which require high labor costs and time costs. Aiming at the limitation of traditional methods, this study proposes a named entity recognition model based on GRU (Gated Recurrent Unit). This model uses word vector as input unit, extracts features through bi-directional GRU layer, and obtains label sequence through output layer. In this study, this model has been tested on a specific domain named entity. The experimental results show that the recurrent neural network model of the article can identify the named entities well, and can save the tedious work of designing the features manually and provide the end-to-end identification method.