Research on Emerging Technology Identification Methods Based on a Knowledge Graph of High-Value Patents

In the context of a new wave of scientific and technological revolution and industrial transformation, this study proposes an emerging technology identification framework that integrates a High-Value Patent Knowledge Graph with Social Network Analysis, aiming to systematically uncover the semantic and structural relationships embedded in patent data and to support national efforts to secure strategic technological advantages. First, patent textual feature scores are extracted using the Doc2Vec model, while indicator feature scores are calculated across the technical, legal, and economic dimensions using the CRITIC weighting method. These two types of scores are then integrated to derive a comprehensive patent value score, and high-value patents are screened according to the Pareto principle. Subsequently, a High-Value Patent Knowledge Graph is constructed based on entity extraction using the BERT-BiLSTM-CRF model and relationship matching techniques. Building upon this graph, centrality analysis is conducted on the nodes, and the results are combined with the rich semantic relationships represented in the knowledge graph to further identify emerging technologies. Taking the New Energy Vehicle domain as an empirical case, a High-Value Patent Knowledge Graph comprising seven types of entities, six types of relationships, and 25,611 triplets is developed, through which six key emerging sub-technology directions are identified. The empirical findings demonstrate the effectiveness and robustness of the proposed approach for emerging technology identification.