With the proposal of the Belt and Road Initiative, research in the field of Silk Road trade has once again become a hotspot. This paper focuses on Named Entity Recognition (NER) for Silk Road Trade Texts Based on Deep Learning for Silk Road trade texts to support the knowledge graph development process. To address the absence of standardized datasets and noise interference in Silk Road trade research, we construct an annotated corpus by aggregating multi-source raw data, deploying DeepSeek-R1’s domain adaptation for noise filtering, followed by LangChain-based paragraph-level semantic segmentation to support NER training. Subsequently, it proposes an innovative deep learning model named BERT-BiGRU-Attention-CRF for entity recognition in Silk Road trade texts. This model synergistically combines BERT’s deep semantic understanding, BiGRU’s capability for modeling long-range dependencies, the attention mechanism’s focus on critical information, and CRF’s optimization of label sequence constraints. Results from comparative experiments with other entity recognition models illustrate that our proposed model exhibits better performance and higher accuracy. It effectively addresses the key challenge of entity recognition posed by long-range dependencies and complex contextual semantics inherent in Silk Road trade texts, thereby laying a solid foundation for constructing high-quality Silk Road trade knowledge graphs.