<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to study the effect of digital media content production and dissemination supported by artificial intelligence technology, we aim to improve the dissemination and influence of digital media in short video platforms. This paper takes digital media as the research object and launches research on digital media content production and dissemination mode. Based on the research method of inquiry description, the core process of digital media intelligent transformation was deeply analyzed, and the overall logical framework of digital media intelligent transformation was designed. The digital media content production flow model is built by combining data integration and aggregation mining from a digital media content perspective. A graph-convolution matrix decomposition recommendation model for digital media broadcasting was developed by analyzing the performance of algorithmic recommendation in digital media applications. The results are presented: in terms of the order of influence degree size, the degree of influence on the number of likes is greater for political news (Beta = 0.2026, p &lt; 0.01) than for military science and technology (Beta = 0.1637, p &lt; 0.01) than for social news (Beta = 0.1225, p &lt; 0.05), i.e.It indicates that the content theme is shifting from traditional social news to political news, which makes it easier to increase the heat of content dissemination. This study helps us think about the impact of media content production and dissemination strategies in the age of mobile Internet and provides new inspiration for communication theory research.</p>
</abstract>

In order to study the effect of digital media content production and dissemination supported by artificial intelligence technology, we aim to improve the dissemination and influence of digital media in short video platforms. This paper takes digital media as the research object and launches research on digital media content production and dissemination mode. Based on the research method of inquiry description, the core process of digital media intelligent transformation was deeply analyzed, and the overall logical framework of digital media intelligent transformation was designed. The digital media content production flow model is built by combining data integration and aggregation mining from a digital media content perspective. A graph-convolution matrix decomposition recommendation model for digital media broadcasting was developed by analyzing the performance of algorithmic recommendation in digital media applications. The results are presented: in terms of the order of influence degree size, the degree of influence on the number of likes is greater for political news (Beta = 0.2026, p < 0.01) than for military science and technology (Beta = 0.1637, p < 0.01) than for social news (Beta = 0.1225, p < 0.05), i.e.It indicates that the content theme is shifting from traditional social news to political news, which makes it easier to increase the heat of content dissemination. This study helps us think about the impact of media content production and dissemination strategies in the age of mobile Internet and provides new inspiration for communication theory research.

Research on Digital Media Content Production and Dissemination Mode Based on Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to study the effect of digital media content production and dissemination supported by artificial intelligence technology, we aim to improve the...

Research on Digital Media Content Production and Dissemination Mode Based on Artificial Intelligence

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 04. März 2024

Akzeptiert: 10. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1477

Schlüsselwörter
Graph convolution, Matrix decomposition, Recommendation model, Digital media

© 2024 Shiming Ma, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Digital Media Content Production and Dissemination Mode Based on Artificial Intelligence

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 04. März 2024

Akzeptiert: 10. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1477

SchlüsselwörterGraph convolution, Matrix decomposition, Recommendation model, Digital media

© 2024 Shiming Ma, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Graph convolution, Matrix decomposition, Recommendation model, Digital media