基于动态专利嵌入表示的AI产业技术融合预测研究  

Predicting AI Industry Technology Convergence Based on Dynamic Patent Embedding Representation

作　　者：张晓黎 匡衡 Zhang Xiaoli;Kuang Heng(School of Statistics and Information,Shanghai University of International Business and Economics,Shanghai 201620,China)

机构地区：[1]上海对外经贸大学统计与信息学院,上海201620

出　　处：《数据分析与知识发现》2025年第3期96-105,共10页Data Analysis and Knowledge Discovery

基　　金：上海市哲学社会科学规划课题(项目编号:2019BGL003)的研究成果之一。

摘　　要：【目的】提出文本嵌入动态图神经网络的链路预测方法,旨在对人工智能领域技术创新的融合趋势进行建模与预测,揭示潜在的技术关联和创新路径。【方法】将专利摘要文本融入动态图神经网络的节点特征表示中,借助动态图神经网络的学习能力得到更准确的链路预测结果。【结果】以国内AI领域为例进行验证,相较于相似度模型和传统图表示学习模型,本文方法的AUC提升约0.06。【局限】由于嵌入维度过大,难以与图神经网络融合,专利摘要文本嵌入时未使用大语言模型。【结论】该方法预测准确度高,增强了对未来AI领域专利融合预测的可信度,是开展细粒度链路预测的一种有效方法。[Objective]This study proposes a link prediction method based on dynamic graph neural networks for text embedding,aiming to model and predict the integration trend of technological innovation in artificial intelligence.It also reveals potential technological connections and innovation pathways.[Methods]We integrated patent abstract texts into the node feature representations of the dynamic graph neural networks.By leveraging the learning capabilities of dynamic graph neural networks,we obtained more accurate link prediction results.[Results]Using the domestic AI field as an example,the method achieved an AUC index improvement of approximately 0.06 compared to similar and traditional graph representation learning models.[Limitations]Due to the high dimensionality of embeddings,it is difficult to integrate with graph neural networks.We did not use large language models for embedding patent abstracts.[Conclusions]The proposed method has a high predictive accuracy,enhancing the credibility of AI patent convergence forecasting.It is an effective way to predict fine-grained links.

关 键 词：细粒度链路预测 技术融合 专利摘要 文本嵌入 动态图神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] G202[自动化与计算机技术—计算机科学与技术]