Abstract: Text segmentation and word embedding are usually the first step in Chinese named entity recognition,but there is no clear delimiter between Chinese words and words.OOV(out of vocabulary) words such as professional words and uncommon words are severely disturbing the computation of word vectors.Model performance based on word vector embedding is highly susceptible to word segmentation effects.At the same time,most of the existing models use low-speed recurrent neural network which is difficult to meet the requirements of industrial applications.Aiming at the above problems,this paper constructs a named entity recognition model based on attention mechanism and convolutional neural network:LAC-DGLU.To handel the problem of word segmentation,this paper proposes a word embedding algorithm based on Local Attention Convolution (LAC),which alle-viates the dependence of the model on the effect of word segmentation.For the problem of slow calculation speed,this paper uses aconvolutional neural network with gate structure:Dilated Gated Linear Unit (DGLU) to improve the speed of model calculation.The experimental results on several datasets show that the model can increase the F1 value by 0.2% to 2%compared with the existing mainstream model,and the calculation speed can reach more than 1.4to 1.9 times of the existing mainstream model.

Key words: Character embedding, Dilated convolution, Gated linear unit, Local attention convolution, Residual structure