Abstract: In the context of Sino-US technology competition,the technology field confronts the challenge of managing the non-li-near overlap of traditional and emerging risks,as well as the deep coupling of endogenous vulnerabilities and external threats.Multiple risks are intertwined and concealed in the technological intelligence network.Existing research primarily focuses on detecting individual risk features,with inadequate attention paid to cross-layer risk coupling modeling and dynamic semantic representation.Faced with that,this study proposes a method for recognizing technology risk structures based on multi-granularity semantic dual reflection,and constructs a potential multi-granularity coupled technology risk structure mining framework driven by both knowledge and learning.Firstly,it extracts scientific information layer by layer from multi-source technology texts and constructs a multi-layer and multi-granularity technology network.Next,eight centrality indicators are used to identify explicit risk structures within the multi-layer and multi-granularity network.However,while centrality-based methods offer high accuracy,they have an inherent limitation in covering concealed risk structures.Therefore,this study introduces a dual reflection approach based on substructure facts and large language models(LLMs).By modeling the high-order semantic features of technology risk structures and establishing a dual reflection mechanism,the mining of technology risks carried by key substructures is realized.This method is capable of constructing risk structure feature patterns leveraging adaptive clustering.A dynamic threshold strategy is utilized to screen potentially unreliable prediction results,and they are corrected through a reflection mechanism driven by differences in risk feature patterns.Meanwhile,the pre-trained knowledge of LLMs is used to integrate multi-model evaluation results,realizing the secondary optimization of decisions guided by LLMs.The methods based on centrality and dual reflection are complementary and synergistic,achieving balanced optimization between accuracy and coverage in recognition results.Experimental results on two private datasets(Tech-Graph and Teen-Graph) and one public dataset(Citeseer) demonstrate the effectiveness of the proposed method.The F1 score on the English technology risk structure dataset reaches 99.55%,representing an improvement of 2.88%.

Key words: Risk structures recognition, Substructure recognition, Technology risk feature patterns, Large language models, Dual reflection