To address the problem that relevant models typically only model characters and relevant vocabulary without fully utilizing the unique glyph structure information and entity type information of Chinese characters， a model that integrates prior knowledge and glyph features for Named Entity Recognition （NER） task was proposed. Firstly， the input sequence was encoded using a Transformer combined with Gaussian attention mechanism， and the Chinese definitions of entity types were obtained from Chinese Wikipedia. Bidirectional Gated Recurrent Unit （BiGRU） was used to encode the entity type information as prior knowledge， which was combined with the character representation using an attention mechanism. Secondly， Bidirectional Long Short-Term Memory （BiLSTM） network was used to encode the long-distance dependency relationship of the input sequence， and a glyph encoding table was used to obtain traditional Chinese characters’ Cangjie codes and simplified Chinese characters’ modern Wubi codes. Then， Convolutional Neural Network （CNN） was used to extract glyph feature representations， and the traditional and simplified glyph feature representations were combined with different weights， which were then combined with the character representation encoded by BiLSTM using a gating mechanism. Finally， decoding was performed using Conditional Random Field （CRF） to obtain a sequence of named entity annotations. Experiment results on the colloquial dataset Weibo， the small dataset Boson， and the large dataset PeopleDaily show that， compared with the baseline model MECT （Multi-metadata Embedding based Cross-Transformer）， the proposed model has the F1 value increased by 2.47， 1.20， and 0.98 percentage points， respectively， proving the effectiveness of the proposed model.