Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting

Abstract

China’s coast is the main spatial carrier and important geographical unit for future industrial development in China. We propose a weighted average decomposition method based on three basic problems in the theory of SDA model decomposition method, investigate the relevant properties of this method, and prove that two decomposition methods widely used in the literature—bipolar decomposition method and midpoint power decomposition method—are the approximate solutions of this decomposition method. Based on the idea of the Wasserstein distance algorithm for machine learning, the Wasserstein distance algorithm and its solution are improved by using the matrix expansion Sinkhorn algorithm and the entropy regularization constraint method, and the industry copolymerization index is constructed through hypothesis testing and Monte Carlo simulation to measure the level of industry copolymerization in coastal China. The results show that the level of industry copolymerisation within the same quantile industry is greater than that between industries across quantile industries in China’s coasts.