Abstract: The span-based model is the primary approach for nested named entity recognition, which is based on the principle of transforming from entity recognition to span classification. However, Chinese datasets characterized by no obvious word delimiters contain ambiguous semantic and boundary information, and thus cause a poor performance of Chinese nested named entity recognition. To address the problem, this paper proposes FCG-NNER, a span-based Chinese nested named entity recognition algorithm fused with glyph information. First, a convolutional neural network is employed to extract the glyph information of Chinese characters. Then, the original information and glyph information are fused by using the cross-biaffine bilinear decoding layer. A fusion CNN layer is utilized to capture local interactions between different spans. Finally, the sum of the output of the cross-biaffine bilinear decoding layer and that of the fusion CNN layer is treated as the input of the fully connected layer to obtain the final prediction results. Two representative Chinese nested named entity recognition datasets, CMeEE and CLUENER2020, are selected for verification. The results show FCG-NNER achieves an accuracy of 65.02%, a recall of 67.93%, and an F1-score of 0.664 4 in the CMeEE dataset while it records an accuracy of 79.45%, a recall of 82.33%, and an F1-score of 0.808 6 in CLUENER2020 dataset, demonstrating FCG-NNER algorithm clearly outperforms the baselines provided by the two datasets.