## 1 Introduction
Moreover, the potential word formation knowledge between the glyphs of adjacent characters can enrich the semantic information of Chinese characters and alleviate the problem of polysemy. For instance, in \"\(\mathcal{\mathbb{J}}\)\" (often representing an action related to the hand), the radical of \"\(\mathcal{\mathbb{R}}\)\", can help to express that \"\(\mathcal{\mathbb{H}}\)\" is a verb, indicating the meaning of walking. However, most of the existing Chinese NER models focus on the contextual semantic features of the character, and ignore the rich semantic information contained in the Chinese character glyph. In addition, the simple concatenation of contextual semantic features from text and glyph features from images can easily cause information redundancy, and the model cannot make full use of the complementary information between different modal features. This multimodal feature fusion problem mainly focuses on video question answering [11], multimodal sentiment analysis [12], etc. In order to fully fuse features from different modals, [PERSON] et al. successively proposed a tensor fusion network [13], memory fusion network [14] and dynamic fusion graph [15]. Further, [PERSON] et al. [16] proposed MuIT, which uses multiple crossmodal Transformers to fuse the three modal features of text, audio and vision. At the heart of MuIT is the use of a crossmodal attention mechanism to repeatedly strengthen the features from two modals, so that MuIT can fully learn useful and complementary information from different modals. However, these methods focus on the fusion of sentence-level features, while the Chinese NER task needs to complete the fusion of character-level features. The questions of how to better utilize the semantic knowledge contained in Chinese character glyphs and their sequences, and how to better integrate contextual semantic features from text and glyph features from images, are two of the main challenges facing Chinese NER currently. Therefore, in order to make full use of the semantic knowledge contained in Chinese character glyphs and fuse the extracted semantic features of glyphs into the character-based Chinese NER model to improve the recognition performance of the model, this paper proposes a novel Chinese NER model based on contextual representation and glyph representation, named CGR-NER. First, for the contextual representation, CGR-NER uses BERT to dynamically generate contextual semantic representations of characters to solve the polysemy problem; for the glyph representation, a hybrid neural network CBHNN, which combines a three-dimensional convolutional neural network and bi-directional long short-term memory network, is proposed to extract the semantic information contained in a Chinese character glyph, the potential word formation knowledge between adjacent glyphs and the contextual semantic and global dependent features of glyph sequences from the perspective of images. Second, to make the representations from different modals complement each other and dynamically adjust the contributions of different representations in different contexts, an interactive fusion method with a crossmodal attention and gate mechanism is proposed, so as to realize the information interaction between the contextual representation and glyph representation and generate a fusion representation. Finally, the fusion representation is input into the BiLSTM-CRF network for Chinese named entity recognition. In summary, the main tasks and contributions of this paper are as follows:  
* A novel hybrid neural network CBHNN is proposed to extract and utilize the rich semantic knowledge contained in Chinese character glyphs and glyph sequences. * An interactive fusion method with a crossmodal attention and gate mechanism is proposed to realize the feature complementarity among different modal representations and dynamically control the fusion of contextual representation and glyph representation.