Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path
Cultural communication plays a vital role in social development and human interaction. Red culture, as an integral part of China’s revolutionary history and socialist construction, holds significant meaning and exerts a wide influence. However, in the era of information technology, effect...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10328606/ |
| _version_ | 1797401041995563008 |
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| author | Junbo Yi Yan Tian Yuanfei Zhao |
| author_facet | Junbo Yi Yan Tian Yuanfei Zhao |
| author_sort | Junbo Yi |
| collection | DOAJ |
| description | Cultural communication plays a vital role in social development and human interaction. Red culture, as an integral part of China’s revolutionary history and socialist construction, holds significant meaning and exerts a wide influence. However, in the era of information technology, effectively disseminating red culture and stimulating public interest and participation has become an urgent challenge. In this study, we use the advanced deep learning tech to explore the use of multimodal data fusion for enhancing the effectiveness and impact of red culture communication. Specifically, we extract text features and image features from users’ browsing information using BI-GRU and CNN, respectively. These features are then fused with user portraits to create a multi-source information fusion vector. Subsequently, we employ a BPNN (Backpropagation Neural Network) to perform user interest classification based on the fused features. Experimental results demonstrate that our proposed user recognition framework achieves an average recognition rate of 95.4% across three types of users, indicating high accuracy. Therefore, the user interest classification model, incorporating fused multi-features, presented in this paper offers a promising approach for future red culture communication, as well as user intelligent recommendation and analysis. |
| first_indexed | 2024-03-09T02:04:10Z |
| format | Article |
| id | doaj.art-c86ce8a9c65549f99303e8e07d5fa4a5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T02:04:10Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c86ce8a9c65549f99303e8e07d5fa4a52023-12-08T00:07:03ZengIEEEIEEE Access2169-35362023-01-011113411813412510.1109/ACCESS.2023.333641910328606Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy PathJunbo Yi0https://orcid.org/0009-0002-8620-9851Yan Tian1Yuanfei Zhao2School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, ChinaAgency Unit, Wuhan Natural Resources and Planning Bureau, Wuhan, ChinaMarketing Department, Hubei Piesat Information Technology Company Ltd., Huanggang, ChinaCultural communication plays a vital role in social development and human interaction. Red culture, as an integral part of China’s revolutionary history and socialist construction, holds significant meaning and exerts a wide influence. However, in the era of information technology, effectively disseminating red culture and stimulating public interest and participation has become an urgent challenge. In this study, we use the advanced deep learning tech to explore the use of multimodal data fusion for enhancing the effectiveness and impact of red culture communication. Specifically, we extract text features and image features from users’ browsing information using BI-GRU and CNN, respectively. These features are then fused with user portraits to create a multi-source information fusion vector. Subsequently, we employ a BPNN (Backpropagation Neural Network) to perform user interest classification based on the fused features. Experimental results demonstrate that our proposed user recognition framework achieves an average recognition rate of 95.4% across three types of users, indicating high accuracy. Therefore, the user interest classification model, incorporating fused multi-features, presented in this paper offers a promising approach for future red culture communication, as well as user intelligent recommendation and analysis.https://ieeexplore.ieee.org/document/10328606/Red culturecultural transmissionBI-GRUCNNfeature fusion |
| spellingShingle | Junbo Yi Yan Tian Yuanfei Zhao Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path IEEE Access Red culture cultural transmission BI-GRU CNN feature fusion |
| title | Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path |
| title_full | Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path |
| title_fullStr | Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path |
| title_full_unstemmed | Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path |
| title_short | Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path |
| title_sort | design of red culture retrieval system based on multimodal data fusion and innovation of communication strategy path |
| topic | Red culture cultural transmission BI-GRU CNN feature fusion |
| url | https://ieeexplore.ieee.org/document/10328606/ |
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