Analysis of the value of news and cultural communication using multimodal learning

News serves as a critical medium for mass communication, characterized by its distinct purpose, comprehensiveness, and authenticity. It is essential to explore the developmental trends within the news sector from the perspective of communicative value and to provide appropriate guidance. This study employs multimodal learning techniques to develop a new content detection model that leverages multimodal fusion for effective content identification. Building on this framework, a sentiment analysis model utilizing multimodal data is constructed to assess the emotional impact of news communication. Additionally, the cognitive mediation model is used to establish an analysis model for determining the value of news communication. Empirical analyses indicate that the multimodal news content detection model presented in this study achieves high-performance metrics, with each index exceeding 0.9. Similarly, the sentiment direction model demonstrates robust accuracy, precision, recall, and F1 values, all-surpassing 0.83. The models exhibit Pearson correlation coefficients (Corr) of 0.758 and 0.785 and mean absolute errors (MAE) of 0.624 and 0.948, respectively, outperforming comparable models. Further analysis into the value of news and cultural communication reveals significant impacts of sound attention (0.328), sound-associated thinking (0.210), and image attention (0.284) on communicative value. Conversely, leisure Internet time shows a negative correlation with knowledge acquisition (-0.090). The findings underscore a positive correlation between the value of news and cultural communication and sound content, thereby affirming that the methodologies employed in this study provide a viable approach to analyzing the communicative value of news. This study not only advances the theoretical understanding of news communication but also enhances practical approaches to media analysis.