Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy Path

• Published in: IEEE access Vol. 11; pp. 134118 - 134125
• Main Authors: Yi, Junbo, Tian, Yan, Zhao, Yuanfei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Back propagation networks; BI-GRU; Classification; Clustering algorithms; CNN; Communication; Convolutional neural networks; Cultural aspects; cultural transmission; Culture; Data integration; Data mining; Feature extraction; feature fusion; History; Human factors; Human relations; Image retrieval; Machine learning; Public participation; Red culture; Social factors; Videos
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3336419

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.