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<p>This paper firstly analyzes the basic composition of intercultural communication ability, discusses the specific function of cultural promotion of urban tourism, and gives the promotion effect of intercultural communication on urban tourism. Secondly, the connotation of multimodal discourse analysis is given, and the technical analysis of text, audio, and visual features is carried out for the analysis of urban tourism discourse. Then, the TF-IDF algorithm is used to realize the feature extraction of tourism culture text, the MFCC algorithm is used to extract the audio features of tourism culture, and the modal classification network is used to realize the recognition of the visual features of urban tourism culture video. Finally, to verify the effectiveness of the application of multimodal discourse analysis in urban cross-cultural communication, three aspects were tested and analyzed respectively. The results show that the F1 value of the TF-IDF algorithm is 0.912, which is 17.07% higher than that of the CTF-TF-IDF algorithm. When the amount of audio recognition is 5GB, the recognition time of the MFCC audio recognition method is 10.4 s. When the weight value of the visual feature extraction network is set to 1.0, the highest visual feature extraction error rate is only 3.96%. Using multimodal discourse analysis to carry out urban tourism discourse analysis can realize more comprehensive urban tourism feature extraction, help tourists strengthen their tourism perception, and then promote the enhancement of urban cross-cultural communication ability.</p>
</abstract>

This paper firstly analyzes the basic composition of intercultural communication ability, discusses the specific function of cultural promotion of urban tourism, and gives the promotion effect of intercultural communication on urban tourism. Secondly, the connotation of multimodal discourse analysis is given, and the technical analysis of text, audio, and visual features is carried out for the analysis of urban tourism discourse. Then, the TF-IDF algorithm is used to realize the feature extraction of tourism culture text, the MFCC algorithm is used to extract the audio features of tourism culture, and the modal classification network is used to realize the recognition of the visual features of urban tourism culture video. Finally, to verify the effectiveness of the application of multimodal discourse analysis in urban cross-cultural communication, three aspects were tested and analyzed respectively. The results show that the F1 value of the TF-IDF algorithm is 0.912, which is 17.07% higher than that of the CTF-TF-IDF algorithm. When the amount of audio recognition is 5GB, the recognition time of the MFCC audio recognition method is 10.4 s. When the weight value of the visual feature extraction network is set to 1.0, the highest visual feature extraction error rate is only 3.96%. Using multimodal discourse analysis to carry out urban tourism discourse analysis can realize more comprehensive urban tourism feature extraction, help tourists strengthen their tourism perception, and then promote the enhancement of urban cross-cultural communication ability.

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

Applied Mathematics and Nonlinear Sciences

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

This paper firstly analyzes the basic composition of intercultural communication ability, discusses the specific function of cultural promotion of urban...

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

Published Online: Dec 05, 2023

Page range: -

Received: Feb 01, 2023

Accepted: Jun 15, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01376

Keywords
Multimodal, Tourism discourse analysis, TF-IDF algorithm, MFCC algorithm, Modal classification network

© 2023 Baiying Chen, published by Sciendo

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

The Application of Multimodal Discourse Analysis in Urban Intercultural Communication

Published Online: Dec 05, 2023

Page range: -

Received: Feb 01, 2023

Accepted: Jun 15, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01376

KeywordsMultimodal, Tourism discourse analysis, TF-IDF algorithm, MFCC algorithm, Modal classification network

© 2023 Baiying Chen, published by Sciendo

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

Keywords
Multimodal, Tourism discourse analysis, TF-IDF algorithm, MFCC algorithm, Modal classification network