Analysis and Optimization of Green Rural Cultural Tourism Image A Case Study Utilizing Big Data from Tourists' Photographs
Description
The objective of this study is to explore the fundamental attributes of rural cultural tourism with a focus on green landscapes and to propose strategies for optimizing the rural tourism image. This study centers on rural tourism destinations across China, leveraging publicly available datasets comprising a large volume of tourist photographs sourced from online platforms. Using an enhanced Convolutional Neural Network (CNN) model, the study conducts image recognition to identify elements such as natural scenery and traditional cultural motifs depicted in the photographs, followed by their classification and organization. The improvement process integrates GoogleNet and implements a range of techniques, including the utilization of ReLU 6 activation parameters, batch normalization algorithms, and the principles of transfer learning. Subsequently, several key variables are established, encompassing site characteristics, recommendations for image optimization, tourist satisfaction levels, and levels of tourist participation, among others. Finally, analytical methods such as correlation analysis and multivariate regression analysis are employed to investigate the relationships between these variables and the enhancement of the rural cultural tourism image. The results reveal a correlation coefficient of 0.75 between site characteristics and image optimization recommendations, and 0.68 between site characteristics and tourist satisfaction. Moreover, image optimization recommendations and tourist satisfaction demonstrate a positive correlation with the enhancement of the tourism image, with regression coefficients of 0.42 and 0.38, respectively. These findings underscore a significant correlation between the distinctive attributes of tourist sites and the recommendations for image optimization, as well as the close association between tourist satisfaction and site characteristics. It is suggested that professional recommendations for image optimization and heightened levels of tourist satisfaction can effectively contribute to the enhancement of the rural cultural tourism image.