This study constructs a named entity recognition (NER) model suitable for the bone-sign interpretations of Han Chang’an City to solve the problem of the inability to classify some bone-sign interpretations due to the lack of key content. The original text of the bone-sign interpretations of Han Chang’an City is used as the dataset, and the begin, inside, outside, end (BIOE) annotation method is utilized to annotate the bone-sign interpretation entities. A multi-feature fusion network (MFFN) model is proposed, which not only considers the structural features of individual characters but also integrates the structural features of character-word combinations to enhance the model’s comprehension of the bone-sign interpretations. The experimental results demonstrate that the MFFN model can better identify the named entities of the bone-sign interpretations of Han Chang’an City and classify the bone-sign interpretations, outperforming existing NER models. This model provides historians and researchers with richer and more precise data support.