Regional forest wellness travel progression potentiality assessment method and enhancement path

Article type: Research Article

Authors: Xu, Qian^{; *}

Affiliations: College of Tourismn, Henan University of Animal Husbandry and Economy, Zhengzhou, China

Correspondence: [*] Corresponding author. Qian Xu, College of Tourism, Henan University of Animal Husbandry and Economy, Zhengzhou 450000, China. E-mail: [email protected].

Abstract: As the problem of sub-health continues to expand among urban residents, forestry tourism has been further developed, and forest wellness travel for the purpose of recuperation has gradually become the focus of transformation and upgrading of the current big health industry. In order to refine the evaluation of the development potential of regional forest health tourism and achieve further promotion of regional forest health tourism, the study first established the construction principles of the evaluation system, combined with expert consultation and theoretical analysis methods to select evaluation indicators, and used analytic hierarchy process to obtain the weight of each indicator. An adaptive variational genetic algorithm was then proposed to improve the BP neural network to form the AGA-BP model, which was finally applied to the assessment of the progression potentiality of forest wellness travel. The outcomes demonstrate that among the assessment indicators of forest wellness travel progression potentiality, the environmental quality has the largest weight of 0.4598; the convergence and precision of the AGA-BP model proposed by the research have been upgraded by 80% and 50% respectively, with a faster global search speed; in the assessment of the regional forest wellness travel progression potentiality, the method is highly consistent with the actual assessment outcomes, with an average precision rate of 98% indicating that it can accurately and effectively conduct potentiality assessment, providing a methodological reference for the sustainable progression of forest wellness travel.

Keywords: Forest recreation, tourism, progression potentiality, BP neural network

DOI: 10.3233/JIFS-230582

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1223-1234, 2023