<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The rich traditional culture is an inexhaustible treasure trove of resources for the brand operation of the modern tourism industry. This paper proposes an improved method for the LDA model of text big data mining, optimizing the topic representation of LDA by introducing the Topic2Vec model, optimizing the probability calculation of generated words by combining the original Dilley distribution and Bernoulli distribution and optimizing the assignment of topic words in the Gibbs adoption process of LDA model with the help of likelihood function. Then, the big data of tourism brand design text, promotion, and cultural products are mined and analyzed based on the improved LFV-LDA model. In the design of tourism brands focusing on traditional culture, the frequency of the keyword “pattern” was the highest, accounting for 8.20%. In brand promotion, the most distributed channel from 2017 to 2021 was a live broadcast, and the growth rate in 2020 and 2021 reached 22.71% and 36.29%. Regarding traditional cultural and creative products, tourism cultural and creative products have maintained a high growth momentum from 2012 to 2021, with an average annual growth rate of 154.25%. Based on the analysis of big data, it can guide the operation of tourism industry brands in all aspects, such as design, promotion, and landing, and give full play to the value connotation of traditional culture.</p>
</abstract>

The rich traditional culture is an inexhaustible treasure trove of resources for the brand operation of the modern tourism industry. This paper proposes an improved method for the LDA model of text big data mining, optimizing the topic representation of LDA by introducing the Topic2Vec model, optimizing the probability calculation of generated words by combining the original Dilley distribution and Bernoulli distribution and optimizing the assignment of topic words in the Gibbs adoption process of LDA model with the help of likelihood function. Then, the big data of tourism brand design text, promotion, and cultural products are mined and analyzed based on the improved LFV-LDA model. In the design of tourism brands focusing on traditional culture, the frequency of the keyword “pattern” was the highest, accounting for 8.20%. In brand promotion, the most distributed channel from 2017 to 2021 was a live broadcast, and the growth rate in 2020 and 2021 reached 22.71% and 36.29%. Regarding traditional cultural and creative products, tourism cultural and creative products have maintained a high growth momentum from 2012 to 2021, with an average annual growth rate of 154.25%. Based on the analysis of big data, it can guide the operation of tourism industry brands in all aspects, such as design, promotion, and landing, and give full play to the value connotation of traditional culture.

Research on brand operation strategy of integrating traditional culture into the modern tourism industry based on big data analysis

Applied Mathematics and Nonlinear Sciences

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

The rich traditional culture is an inexhaustible treasure trove of resources for the brand operation of the modern tourism industry. This paper proposes an...

Research on brand operation strategy of integrating traditional culture into the modern tourism industry based on big data analysis

Pubblicato online: 18 ago 2023

Pagine: -

Ricevuto: 12 nov 2022

Accettato: 27 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00214

Parole chiave
Big data, LFV-LDA model, Topic representation, Tourism branding, Theme words

© 2023 Xiang Zhao et al., published by Sciendo

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

Research on brand operation strategy of integrating traditional culture into the modern tourism industry based on big data analysis

Pubblicato online: 18 ago 2023

Pagine: -

Ricevuto: 12 nov 2022

Accettato: 27 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00214

Parole chiaveBig data, LFV-LDA model, Topic representation, Tourism branding, Theme words

© 2023 Xiang Zhao et al., published by Sciendo

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

Parole chiave
Big data, LFV-LDA model, Topic representation, Tourism branding, Theme words