A Computational Framework for Optimizing Technology Transfer Systems in Applied Universities Using Big Data and Artificial Intelligence

Abstract

Technology transfer plays a pivotal role in translating academic research into practical applications, particularly in applied universities. This paper presents a comprehensive computational framework that leverages big data analytics and artificial intelligence to address longstanding challenges in technology commercialization. By integrating temporal convolutional networks with graph neural networks, we develop a sophisticated technology assessment model that achieves 94.2% prediction accuracy. The framework incorporates BERT-based semantic analysis for precise technology-industry matching and implements blockchain smart contracts for secure intellectual property management. Experimental results demonstrate a 42% improvement in technology commercialization rates and a 58% reduction in processing time. This research contributes both theoretical foundations and practical solutions for enhancing technology transfer efficiency in academic institutions.