Abstract: [Purposes] Remote sensing image target recognition and knowledge graph technology have become one of the important technical methods for the update and application of real-scene 3D data.However，there are still problems such as low automation level，large amount of data annotation，and inconsistent relationship models and classification systems in the extraction of the association relationships between remote sensing image target entities. [Methods] This paper proposes a technical framework for extracting the relationship between remote sensing image target entities based on the DeepSeek large model.Through the construction of relationship models and category systems and the production of remote sensing image entity relationship data based on prompt engineering，the construction of remote sensing image entity knowledge graphs is completed.To verify the effectiveness of the proposed method，experiments were conducted on a dataset of 3392 high-resolution remote sensing images，focusing on relation extraction for typical complex surface targets. [Findings] The results show that the overall F1 value of the relationship extraction of this method on mainstream large models is above 0.85. [Conclusions] This method can quickly adapt to different remote sensing scenarios，is flexible and convenient to operate，and provides structured knowledge support for tasks such as remote sensing recognition and geographic question answering，promoting the transformation of remote sensing intelligent interpretation from the “feature perception” paradigm to the “knowledge reasoning”paradigm．

Key words: remote sensing target recognition, entity relationship, knowledge graph, DeepSeek, relationship extraction