Abstract: 【Purpose/significance】The current research mainly identifies technological opportunities from the perspective of topic rec⁃ ognition and evolution and technology combination, but it is still needed to improve the accuracy and interpretability of results from the perspective of more granular "structure-function" correlation among technological elements【. Method/process】In this paper, based on the entity relationship extraction method, the structure-functional technical element entities and the various relationships between entities are obtained, and the functional entities are clustered and integrated. Then, this paper constructs a knowledge graph based on structure-function semantic association, and regards the structure and structure combination with direct semantic association with function as technical opportunities, and uses the link prediction model KG-Predict to identify technical opportunities and evaluate the results. Finally, the path of technology opportunity realization is obtained by obtaining the link of the technology opportunity in the knowledge graph【. Result/conclusion】This paper uses patent data in the field of vacuum cleaning robot for empirical analysis, and the experimental results show that the MRR evaluation index for technological opportunity identification is 0.135, and Hits@10 is 0.265, which are 0.064 and 0.123 higher than that of the method without semantic correlation between technical elements, proving the effec⁃ tiveness of the research method in this paper. In addition, the technology opportunities are explained in detail according to the path to realization【. Innovation/limitation】Identify technology opportunities from the perspective of structure and function, and obtain the real⁃ ization path of technology opportunities based on the knowledge graph. Further studies can further refine the structural relationship and select better models to improve accuracy.