Article: The analysis of Chinese red classic literature communication culture tourism based on intelligent long short-term memory networks and deep learning Journal: International Journal of Arts and Technology (IJART) 2026 Vol.16 No.9 pp.163 - 181 Abstract: This study aims to improve the emotion recognition accuracy in red classic literature and promote the effect of visual dissemination. To this end, an emotion recognition model based on bidirectional encoder representations from transformers (BERT) - bidirectional encoder representations from transformers (BiLSTM)-SentiWordNet (SWN). This model combines BERT's deep semantic representation, BiLSTM's bidirectional time series processing capabilities, and SentiWordNet's emotion enhancement module. Through comparative experiments, it can be found that the constructed BERT-BiLSTM-SWN model's accuracy attains 95.19%, about 5% higher than other model algorithms. Therefore, the proposed algorithm shows excellent performance in the emotion recognition of red classic literature, and can more accurately capture the emotional features of the text, providing a solid technical foundation for visual dissemination, and further promoting the development of literature digitisation and sentiment analysis technology. Inderscience Publishers - linking academia, business and industry through research

Title: The analysis of Chinese red classic literature communication culture tourism based on intelligent long short-term memory networks and deep learning

Authors: Yuanzhuo Wang; Wenxuan Xiang; Lang Wei

Addresses: The School of Arts, Wuhan University, Wuhan, 430072, China ' The School of Arts, Wuhan University, Wuhan, 430072, China ' The School of Art and Design, Hubei University, Wuhan, 430062, China

Abstract: This study aims to improve the emotion recognition accuracy in red classic literature and promote the effect of visual dissemination. To this end, an emotion recognition model based on bidirectional encoder representations from transformers (BERT) - bidirectional encoder representations from transformers (BiLSTM)-SentiWordNet (SWN). This model combines BERT's deep semantic representation, BiLSTM's bidirectional time series processing capabilities, and SentiWordNet's emotion enhancement module. Through comparative experiments, it can be found that the constructed BERT-BiLSTM-SWN model's accuracy attains 95.19%, about 5% higher than other model algorithms. Therefore, the proposed algorithm shows excellent performance in the emotion recognition of red classic literature, and can more accurately capture the emotional features of the text, providing a solid technical foundation for visual dissemination, and further promoting the development of literature digitisation and sentiment analysis technology.

Keywords: long short-term memory network; red classic literature; visual dissemination; deep learning; BiLSTM.

DOI: 10.1504/IJART.2026.155619

International Journal of Arts and Technology, 2026 Vol.16 No.9, pp.163 - 181

Received: 22 Mar 2026
Accepted: 10 May 2026
Published online: 07 Aug 2026 *

Title: The analysis of Chinese red classic literature communication culture tourism based on intelligent long short-term memory networks and deep learning

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