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<p>Taking cultural ecology as the carrier of tourism resources, it fully embodies the significance of the synergistic development of cultural tourism ecology for the innovation of the cultural tourism model. The article establishes a cultural tourism ecological synergy model by integrating cultural ecology and cultural tourism under the guidance of the theory of the life cycle of tourist places and industrial innovation. Taking the nine provinces of the Yellow River as the research object, its cultural tourism data are collected and pre-processed, cultural tourism keywords are extracted by the TextRank algorithm, and the LDA model is established to obtain cultural tourism themes. The Skip-gram model was combined with the GCN network to establish a cultural tourism emotional image perception model, and the Moran index and nearest-neighbor index were introduced to analyze the spatial structure and regional evolution of cultural tourism attractions. The keywords of cultural tourism are mainly embodied in ecology and culture, which all appear more than 1,000 times, and the percentage of tourists’ most concerned cultural tourism themes is 22.15%, and most of the nearest neighbor indices of the spatial structure of the attractions at different levels are less than 1, which is cohesive. The Moran index of tourists’ footprints in 2022 is 0.106, the footprint coverage rate is 0.26 percentage points higher than that in 2012, and tourists’ positive emotional evaluation of cultural tourism destinations reaches 80%. Based on cultural ecology, realizing the synergistic development of cultural tourism and ecology is the way to innovate cultural tourism.</p>
</abstract>

Taking cultural ecology as the carrier of tourism resources, it fully embodies the significance of the synergistic development of cultural tourism ecology for the innovation of the cultural tourism model. The article establishes a cultural tourism ecological synergy model by integrating cultural ecology and cultural tourism under the guidance of the theory of the life cycle of tourist places and industrial innovation. Taking the nine provinces of the Yellow River as the research object, its cultural tourism data are collected and pre-processed, cultural tourism keywords are extracted by the TextRank algorithm, and the LDA model is established to obtain cultural tourism themes. The Skip-gram model was combined with the GCN network to establish a cultural tourism emotional image perception model, and the Moran index and nearest-neighbor index were introduced to analyze the spatial structure and regional evolution of cultural tourism attractions. The keywords of cultural tourism are mainly embodied in ecology and culture, which all appear more than 1,000 times, and the percentage of tourists’ most concerned cultural tourism themes is 22.15%, and most of the nearest neighbor indices of the spatial structure of the attractions at different levels are less than 1, which is cohesive. The Moran index of tourists’ footprints in 2022 is 0.106, the footprint coverage rate is 0.26 percentage points higher than that in 2012, and tourists’ positive emotional evaluation of cultural tourism destinations reaches 80%. Based on cultural ecology, realizing the synergistic development of cultural tourism and ecology is the way to innovate cultural tourism.

Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Taking cultural ecology as the carrier of tourism resources, it fully embodies the significance of the synergistic development of cultural tourism ecology for...

Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice

Publié en ligne: 05 août 2024

Pages: -

Reçu: 25 mars 2024

Accepté: 19 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2055

Mots clés
TextRank algorithm, LDA model, Skip-gram model, Moran index, Nearest neighbor index, Cultural tourism

© 2024 Li Chu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice

Publié en ligne: 05 août 2024

Pages: -

Reçu: 25 mars 2024

Accepté: 19 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2055

Mots clésTextRank algorithm, LDA model, Skip-gram model, Moran index, Nearest neighbor index, Cultural tourism

© 2024 Li Chu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
TextRank algorithm, LDA model, Skip-gram model, Moran index, Nearest neighbor index, Cultural tourism