Quantitative Study on the Communication Effectiveness of Changsha City Brand Image from “Online Star City” to “Long-term Famous City” Based on Big Data Regression Modeling

Hui Huang 1,2, Naixuan Yang 3, Yuhe Song 4
1School of Economics & Management, Changsha University of Science & Technology, Changsha, Hunan, 410114, China
2
3 School of Design Art, Changsha University of Science & Technology, Changsha, Hunan, 410114, China
4College of Art and Design, Yantai Institute of Science and Technology, Yantai, Shandong, 265600, China

Abstract

This paper constructs an improved Changsha city brand image communication model on the basis of the traditional contagion model, and studies the communication effect of Changsha in the process of city brand image transformation from “online star city” to “long-term famous city”. By summarizing and analyzing the current situation of Changsha’s city brand image communication, the evaluation index system of Changsha’s city brand image communication effectiveness is constructed, and the collected evaluation index data are downscaled using principal component analysis. The support vector regression machine combined with differential evolution algorithm is used to quantitatively analyze the communication benefits of Changsha city brand image. The improved city brand image communication model in this paper has a higher accuracy compared with the traditional contagion model, and can accurately grasp the communication effect of Changsha city brand image. The average relative error of the support vector regression machine model in the quantitative analysis of communication benefits for the test samples from 2020 to 2023 is only 1.53%, which is 27.86% lower than that of the BP neural network model. It strongly demonstrates the effectiveness of the regression model selected based on the communication big data in this paper, and provides a useful reference for accurately measuring the communication benefits of Changsha’s city brand image.

Keywords: city brand image, infectious disease model, principal component analysis, support vector regression machine, differential evolution, communication benefit