A Fuzzy Comprehensive Evaluation Method of Regional Economic Development Quality Based on a Convolutional Neural Network

Abstract

This paper presents an in-depth research analysis on the evaluation of the development quality of regional economy through an improved convolutional neural network algorithm, and uses it to design a fuzzy comprehensive evaluation model for the practical process. Based on the measured indices of different variables, a spatial econometric model is constructed and provincial panel data are selected to empirically analyze the impact and spatial spillover effects of financial agglomeration and technological innovation on regional economic quality development from both static and dynamic aspects and to examine the spatial correlation of the factors. A new serial data flow model is adopted, which optimizes the control of data flow in convolutional computation, reduces the percentage of clock cycles used to read memory data, and increases the computational efficiency. At the same time, with dynamic data caching, a convolutional computation can be completed in one clock cycle, reducing the memory capacity required for caching intermediate data. The effectiveness of the evaluation system constructed in this paper is further tested. Most of the indicators have a significant positive or negative impact on the quality level of economic development, and the direction of the impact is consistent with the positive and negative attributes of the indicators in this study, which verifies the validity of the evaluation indicator system constructed in this paper. In summary of the study, effective suggestions are made in terms of human capital investment, reasonable allocation of fiscal expenditure, enhancing regional greening development and improving risk prevention measures.