A Technical Framework for Data-Driven Industrial Transformation in Northeast China

Abstract

This study develops a dynamic evaluation framework to analyze the interaction between digital and traditional industries in Northeast China from temporal and regional perspectives. Using a 24-year dataset (2000–2024) across five major Chinese economic regions, the framework integrates coupling coordination modeling, system dynamics, and time-series forecasting with SARIMA and LSTM models. Results indicate that digital investment and human capital development significantly enhance industrial performance, with strong positive correlations between coordination and regional growth (r = 0.89, p < 0.01). The LSTM model demonstrates superior adaptability to nonlinear fluctuations compared with SARIMA, achieving a 25% reduction in forecast error. Evidence from Liaoning Province validates that targeted policy interventions – particularly demonstration zone strategies – can accelerate coordination between digital and traditional sectors. The framework provides methodological and empirical foundations for regional development strategies in transitional economies.

Citation