Innovative Research on Industrial Upgrading and Transformation Path of Regional Economy in the Perspective of Digital Economy

Abstract

This paper utilizes the digital economy, regional spatial distribution digital mining technology, establishes the indicator system, assigns values to the indicators, and applies the indicator system to evaluate the development of industrial structure upgrading and transformation of the regional economy. According to the research results of the basic structure and algorithm of the self-organized neural network model, combined with the classification layer smoothing algorithm, the regional spatial clustering model is constructed. Using the constructed model, the regional economic spatial dynamic data is mined, and the path of industrial transformation and upgrading in the regional spatial area is dynamically studied. Furthermore, using the CA algorithm, the evolution law of land use structure in the region is predicted, and the development of the regional economy is inferred. At the same time, through the spatial autocorrelation regional prediction model, the relevant regional spatial expansion prediction method is proposed, the three research hypotheses of this paper are made, and the constructed model is utilized to innovate the exploration of industrial transformation and upgrading paths from the spatial dimension. Using empirical research methods, descriptive statistics, and mediating effect heterogeneity analysis are carried out to analyze the factors that influence regional economic variables in place A. The empirical analysis shows that the fluctuation range interval of industrial structure is between [0.811,10.2805], which brings a large positive impact on the regional economy.2017 national digital economic growth rate changed from -0.17446 to 0.17143 to realize a positive growth rate, and at the same time, there is a heterogeneity in the development of the east-west and central regions of A. The hypotheses presented in this paper are all valid.

Keywords

• spatial data mining
• self-organizing neural network
• spatial clustering model
• ca algorithm
• spatial diffusion prediction
• 11a63