Based on Gated Recurrent network analysis of advanced manufacturing cluster and unified large market to promote regional economic development

Abstract

This study evaluates the catalytic effects of advanced manufacturing industry clusters and unified large markets on regional economic development from a computer science perspective, revealing their underlying mechanisms. It employs a Gated Recurrent Network (GRN) model optimized with Gradient Boosting Decision Tree (GBDT) technology to conduct empirical analysis through comprehensive data collection and analysis. The primary objectives are to assess these catalytic effects, highlight the importance of innovation and environmental indicators, determine the contribution levels of various factors, and test the computational fit and predictive accuracy of the model. Key findings indicate that the GBDT-GRN model demonstrates a significant improvement in data computation accuracy, ranging from 20% to 52%, and an increase in response time by 23% to 52%. The model achieves a computational fit of 92% to 99% when analyzing regional economic development. The proposed GBDT-GRN model is highly accurate and reliable in evaluating catalytic effects, providing strong support for policy-making and business decision-making. Innovation and environmental indicators play a crucial role, with varying contributions from different factors. This study offers an effective solution for sequence data prediction problems, supports policy-making and business decisions, and points to promising directions for future research.