Revolutionizing Economic Growth Analysis: a Novel Computational Approach to Assessing the Influence of Technological Financial Efficiency on Real Economic Growth

In the pursuit of comprehending the intricate relationship between scientific and technological financial efficiency and real economic growth, traditional methodologies have often encountered limitations arising from historical data induction. This study introduces a novel approach by leveraging a computer data association algorithm as a cornerstone for calculating this impact. The efficiency of science and technology finance is quantified by meticulously selecting pertinent indicators and employing the data envelopment analysis method. The utilization of a multi-objective data association tracking algorithm, rooted in fuzzy clustering, allows the extraction of synchronous data change patterns. This research further constructs an intermediary effect analysis model, guided by the principle of intermediary effect analysis, and subsequently validates the results through a Bootstrap intermediary effect test. The empirical analysis illuminates that at the national level, the regression coefficient of science and technology financial efficiency stands at 0.259, underscoring its role in promoting real economic growth. This innovative methodology not only addresses historical limitations but also advances our understanding of the symbiotic relationship between finance, science, and technology, thus offering a robust foundation for future research and policy decisions.