Research on the Cultural and Economic Development of Characteristic Town Based on Questionnaire Survey: A Case Study of Suzhou Embroidery Town

Meng Zhao; Yuxuan Fan

doi:10.54097/va4nxa94

Authors

Meng Zhao
Yuxuan Fan

DOI:

https://doi.org/10.54097/va4nxa94

Keywords:

Suzhou Embroidery Town, K-means, Tourist Characteristics.

Abstract

The Suzhou Embroidery Town serves as a significant base for the inheritance of Suzhou embroidery techniques and cultural tourism, which is a representative of characteristic towns, attracting a large number of tourists. However, its development faces challenges, including uneven visitor experiences, low consumer satisfaction, and insufficient industrial innovation. This study examines Suzhou Embroidery Town, based on data from 2,500 survey questionnaires. The characteristics and values of visitors are analyzed using K-means and Binary Logistic Algorithms. It was found that visitors have a relatively low willingness to purchase handmade products due to various factors. Furthermore, Random Forest and AHP-Entropy Weight Algorithms were used to conduct a multidimensional evaluation of significant factors affecting the cultural and economic development of Suzhou Embroidery Town. The findings reveal that the overall performance of tourism services is good, but the competitive edge of technologically empowered cultural experiences is limited. From the perspectives of cultural and economic development of Suzhou Embroidery Town, this paper summarizes the typical experiences in cultivating characteristic towns, thereby promoting the high-quality economic development of characteristic towns.

Downloads

Download data is not yet available.

References

[1] Ahmed M, Seraj R, Islam S M S. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation [J]. Electronics, 2020, 9(8).

[2] Jia Z, Jiao Y, Zhang W, et al. Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data[J]. Sustainability, 2022, 14(7).

[3] Katahenggam N. Tourist perceptions and preferences of authenticity in heritage tourism: visual comparative study of George Town and Singapore [J]. Journal of Tourism and Cultural Change, 2019, 18(4): 371-385.

[4] M. Ghazal T, Zahid Hussain M, A. Said R, et al. Performances of K-Means Clustering Algorithm with Different Distance Metrics[J]. Intelligent Automation & Soft Computing, 2021, 29(3): 735-742.

[5] Qi J, Lu Y, Han F, et al. Spatial Distribution Characteristics of the Rural Tourism Villages in the Qinghai-Tibetan Plateau and Its Influencing Factors[J]. Int J Environ Res Public Health, 2022, 19(15).

[6] Schonlau M, Zou R Y. The random forest algorithm for statistical learning [J]. The Stata Journal: Promoting communications on statistics and Stata, 2020, 20(1): 3-29.

[7] Warchalska-Troll A, Warchalski T. The selection of areas for case study research in socio-economic geography with the application of k-means clustering [J]. Wiadomości Statystyczne. The Polish Statistician, 2022, 67(2): 1-20.

[8] Zhou F, Zhao F, Xu Q, et al. Evaluation and Selection Methods of Tourism Characteristic Town: The Case of Liaoning Province, China [J]. Sustainability, 2020, 12(13).

[9] Ramires A, Brandão F, Sousa A C. Motivation-based cluster analysis of international tourists visiting a World Heritage City: The case of Porto, Portugal [J]. Journal of Destination Marketing & Management, 2018, 8: 49-60.

[10] Yildirim M E, Kaya M, Furkanince I. A case study: unsupervised approach for tourist profile analysis by k-means clustering in turkey [J]. Journal of Internet Computing and Services, 2022, 23(1): 11-17.

[11] Ruano M, Huang C-Y. A Novel Approach to Service Design within the Tourism Industry: Creating a Travel Package with AHP-TRIZ Integration [J]. Systems, 2023, 11(4).