Wang Hongkai, Song Chao, Li Hongming
School of Jewellery and Art Design, Beijing Institute of Economics and Management, Beijing, 100102, China.
New Media E-commerce School, Chongqing Institute of Engineering, Chongqing, 400056, China.
Sci Rep. 2024 Nov 19;14(1):28661. doi: 10.1038/s41598-024-80378-2.
Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums' social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media content from four different museums is collected, a dataset containing tens of thousands of images is constructed, and a CNN-based model is designed for automatic identification and classification of image content. The model is trained and tested through a series of experiments, evaluating its performance in enhancing museums' social media communication. Experimental results indicate that the CNN model significantly enhances user participation, access rates, retention rates, and sharing rates of content. Specifically, user participation increased from 15 to 25%, reflecting a 66.7% rise. Content coverage increased from 20 to 35%, showing a 75% increase. User retention rate rose from 10 to 20%, indicating a 100% increase. Content sharing rate increased from 5 to 15%, reflecting a 200% rise. Additionally, the study discusses the model's performance across various museum types, batch sizes, and learning rate settings, verifying its robustness and wide applicability.
基于深度媒介化理论和人工智能（AI）技术，本研究探索通过应用卷积神经网络（CNN）技术有效提升博物馆的社交媒体传播效果。首先，收集来自四个不同博物馆的社交媒体内容，构建一个包含数万张图像的数据集，并设计一个基于CNN的模型用于图像内容的自动识别和分类。通过一系列实验对该模型进行训练和测试，评估其在增强博物馆社交媒体传播方面的性能。实验结果表明，CNN模型显著提高了用户参与度、访问率、留存率和内容分享率。具体而言，用户参与度从15%提高到25%，增长了66.7%。内容覆盖率从20%提高到35%，增长了75%。用户留存率从10%提高到20%，增长了100%。内容分享率从5%提高到15%，增长了200%。此外，该研究还讨论了模型在不同博物馆类型、批量大小和学习率设置下的性能，验证了其鲁棒性和广泛适用性。
