Author(s)

HongYu Su

Affiliation(s)

China National Institute of Standardization, Beijing 100191, China.

Corresponding Author

HongYu Su

ABSTRACT

Technology transfer is a critical bridge connecting scientific and technological innovation with industrial application, and its efficiency is largely constrained by the inaccuracy of supply-demand matching and the lack of systematic technical support. With the advancement of computer technologies such as big data, artificial intelligence (AI), and knowledge graphs (KG), digital profiling has emerged as a promising tool to address the aforementioned bottlenecks. However, existing research on the integration of digital profiling and technology transfer lacks in-depth exploration of technical implementation mechanisms, and fails to fully leverage computer technologies to solve core problems such as multi-dimensional feature extraction, dynamic modeling, and intelligent matching.

To fill this gap, this paper conducts systematic theoretical and technical research on technology transfer and digital profiling from a computer science perspective. First, we clarify the theoretical connotation of digital profiling in the context of technology transfer, and construct a three-layer technical framework (data layer, model layer, application layer) based on computer system design principles. Second, we propose a two-dimensional digital profiling method system: for the supply side (technological achievements), we design a feature extraction framework integrating BERT-based text mining and KG construction; for the demand side (enterprises), we develop a demand mining model combining LDA topic modeling and multi-source data fusion. Third, we establish an intelligent supply-demand matching mechanism based on hybrid recommendation algorithms and multi-objective optimization. Finally, we verify the feasibility and effectiveness of the proposed framework through theoretical deduction, algorithm simulation, and experimental validation on real datasets.

The research enriches the theoretical system of technology transfer from the perspective of computer science, and provides a technical paradigm for the digital transformation of technology transfer. The proposed methods and frameworks can effectively improve the accuracy of supply-demand matching, reduce the transaction cost of technology transfer, and lay a foundation for the development of intelligent technology transfer platforms.

KEYWORDS

Technology transfer; Digital profiling; Knowledge graph; Dynamic modeling; Hybrid recommendation; Supply-demand matching; Intelligent engineering

CITE THIS PAPER

HongYu Su. Knowledge graph-enhanced dynamic digital profiling: a technical framework for intelligent supply-demand matching in technology transfer. World Journal of Information Technology. 2025, 3(6): 10-21. DOI: https://doi.org/10.61784/wjit3071.

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