面向产学研融合创新的高校科技成果转化机会挖掘与推荐框架构建

Construction of a Framework for Opportunity Mining and Recommendation of Transformation of Scientific and Technological Achievements in University Oriented to Industry-University-Research Integration and Innovation

摘要: 在高等教育构筑人才培养与科研创新高地进而服务国家战略需求的目标定位下，产学研融合创新引起了社会各界的广泛重视与关注，成为科学研究者和教育与科技管理者都共同关注的热点话题。然而，2023年全国高校科技类课题数达844,835项，但实现技术转让的成果仅28,920项，转化比例仅为3.42%，科技成果的低转化率可见一斑。为破解这一难题，本文以科技交互关联协同创新理论、创新链理论与创新生态理论为支撑，构建了面向科技成果转化的理论与方法框架。该框架旨在综合运用科学计量学、数据挖掘与复杂网络链路预测等方法，通过构建高校科技成果集与产业技术需求集，从交互关联结构与供需匹配机制角度识别潜在转化路径与模式。同时，本框架支持产学研合作伙伴推荐与典型转化机会挖掘，为提升高校科技成果转化效率和优化产学研协同创新体系提供了结构化、可复用的分析工具与决策参考。

Abstract: Under the goal of higher education to build a talent cultivation and scientific research innovation highland and serve the national strategic needs, the integration and innovation of industry, academia, and research has attracted widespread attention and concern from all sectors of society, becoming a hot topic of common concern for scientific researchers and education and science and technology managers. However, in 2023, the number of science and technology projects in national universities reached 844,835, but only 28,920 achieved technology transfer, with a conversion rate of only 3.42%. The low conversion rate of scientific and technological achievements is evident. To solve this problem, this paper, supported by the theories of scientific and technological interaction and collaborative innovation, innovation chain, and innovation ecosystem, constructs a theoretical and methodological framework for the transformation of scientific and technological achievements. This framework aims to comprehensively apply methods such as scientometrics, data mining, and complex network link prediction to identify potential transformation paths and models from the perspective of interaction and association structure and supply-demand matching mechanism by constructing a set of university scientific and technological achievements and an industrial technology demand set. At the same time, this framework supports the recommendation of Industry-Academia-Research partners and the mining of typical transformation opportunities, providing structured and reusable analytical tools and decision-making references for improving the efficiency of university scientific and technological achievement transformation and optimizing the Industry-Academia-Research collaborative innovation system.