面向科技成果转化的专利交易预测研究：基于图神经网络和企业画像的视角

doi:10.3772/j.issn.1000-0135.2026.04.003

情报学报

2026, Vol. 45

Issue (4): 487-503 DOI: 10.3772/j.issn.1000-0135.2026.04.003

情报理论与方法

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面向科技成果转化的专利交易预测研究：基于图神经网络和企业画像的视角

钱明辉^1,2, 赵梦纯¹, 王驰¹, 王名钰¹

1.中国人民大学信息资源管理学院，北京 100872
2.中国人民大学长江经济带研究院，北京 100872

Patent Transaction Prediction for Technology Transfer: Perspective Based on Graph Neural Networks and Corporate Portraits

Qian Minghui^1,2, Zhao Mengchun¹, Wang Chi¹, Wang Mingyu¹

1.School of Information Resource Management, Renmin University of China, Beijing 100872
2.Yangtze River Economic Zone Research Institution of RUC, Beijing 100872

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摘要加快科技成果转化是深入实施创新驱动发展战略、推进经济社会高质量发展的重要举措。本研究提出一种基于企业画像和专利画像的异构图神经网络模型（company-patent profile relational graph convolutional networks，CP-RGCN），用于预测专利交易对象，并在人工智能领域的专利数据上验证该模型效果。研究发现，该模型在MRR（mean reciprocal rank）、Hits@K等指标上均优于基线模型SVM（support vector machine）、RF（random forest）、GCN（graph convolutional network）、RGCN（relational graph convolutional network）、GraphSAGE（graph sample and aggregate）和GAT（graph attention network）。特征重要性分析结果显示，在异构图神经网络的基础上，融合专利画像和企业画像特征可以有效提升专利交易的预测效果，增强模型的可解释性和可信度。本研究提出的CP-RGCN模型有助于提升潜在科技成果转化机会识别的效果，促进科技成果转化效率的提高，也可为异构图神经网络在更多领域和场景的渗透与应用提供参考和依据。

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关键词 ：科技成果转化, 专利交易, 图神经网络, 企业画像

收稿日期: 2025-04-21

作者简介: 钱明辉，1980年生，博士，教授，博士生导师，研究方向为智能信息分析、人工智能治理；赵梦纯，1999年生，博士研究生，研究方向为专利分析；王驰，通信作者，1998年生，博士研究生，研究方向为专利分析，E-mail：ellie@ruc.edu.cn；王名钰，2000年生，硕士研究生，研究方向为专利分析；

引用本文:

钱明辉, 赵梦纯, 王驰, 王名钰. 面向科技成果转化的专利交易预测研究：基于图神经网络和企业画像的视角[J]. 情报学报, 2026, 45(4): 487-503.
Qian Minghui, Zhao Mengchun, Wang Chi, Wang Mingyu. Patent Transaction Prediction for Technology Transfer: Perspective Based on Graph Neural Networks and Corporate Portraits. 情报学报, 2026, 45(4): 487-503.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.04.003 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I4/487

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