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Research Article |

Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis

Traditional information dissemination channels can no longer meet the needs of tourists for in-depth cultural tourism experience. More and more tourists hope to make their own tourism plans through the feedback from other tourists, and management departments also need to make strategies for scenic spot optimization by referring to the feedback from scenic spot experience. This study uses word frequency statistics and sentiment analysis based on deep learning to evaluate the perception of aesthetic, cultural and service values of Sha Mian Island scenic spot, using image semantic cutting to perceive the tendency of architectural photography, discover the shortcomings of the scenic spot and give suggestions for optimisation. The results show that the aesthetic value and greenery level of Sha Mian Island is high, the scenic content is vague, and the image data is not ideal for the perception of human and service content. This study provides a way of research that has a wide range of data sources, is easy to operate and can be quickly calculated and analysed in a short period of time with time-sensitive evaluations and pictures, giving a way of research that provides optimisation solutions for tourist attractions.

Image Semantic, Tourism Perception, Word Frequency Analysis, Sentiment Analysis, Semantic Network Analysis

APA Style

Wei-feng, C., Lei, S. (2023). Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Science Innovation, 11(6), 259-265. https://doi.org/10.11648/j.si.20231106.16

ACS Style

Wei-feng, C.; Lei, S. Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Sci. Innov. 2023, 11(6), 259-265. doi: 10.11648/j.si.20231106.16

AMA Style

Wei-feng C, Lei S. Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Sci Innov. 2023;11(6):259-265. doi: 10.11648/j.si.20231106.16

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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