[Purpose/Significance] Leveraging Named Entity Recognition (NER) techniques for the thorough exploration of ancient literary documents not only drives forward the digitization of ancient Chinese texts, including the vital process of Ancient text digitization, which is crucial for historical studies, bolstering cultural confidence, promoting traditional Chinese culture, and advancing Named Entity Recognition (NER) as a foundational task in NLP. [Method/Process] A method for named entity recognition in classical Chinese texts with multi-granularity feature fusion was proposed, Leveraging "Zuo Zhuan" as the research corpus and formulating named entity recognition tasks for personal names, geographical names, temporal entities, etc. Initially, ancient character information, part-of-speech (POS) information, and glyph features were integrated to enhance input feature representation. Subsequently, auxiliary tasks for predicting entity boundaries were introduced, alongside the utilization of a Transfer Interactor heuristic to learn classical Chinese entity formation rules. This was complemented by joint contextual information extraction using BiLSTM and IDCNN (Iterated Dilated Convolutional Neural Network). Finally, learned features were weighted and merged into a CRF (Conditional Random Field) for entity prediction. [Result/Conclusion] Experimental results demonstrate that the proposed method of multi-granularity feature fusion for named entity recognition in classical Chinese texts enhances precision, recall, and F1 score by 5.09%, 13.45%, and 9.87%, respectively, compared to the mainstream BERT-BiLSTM-CRF method. Multi-granularity feature fusion for named entity recognition in classical Chinese texts is crucial for accurately identifying named entities in ancient texts.