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

<p>This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the correlation algorithm to analyze the influence of TV drama ratings and broadcasting accounted for and selecting cluster analysis to explore the relationship between TV drama broadcasting and TV type and rating. Take the ratings as the dependent variable, set the independent variables, establish the multivariate statistical model, and use SPSS software to calculate factor analysis of TV drama ratings. By combining user opinions, optimize the heterogeneous graph neural network film and television communication model based on attribute information. Test the MAE value and effect of the propagation algorithm proposed in this paper using the real Movies Lens dataset. When N=5, the recall, precision and <italic>F</italic><sub>1</sub> of this paper’s algorithm are 0.295, 0.751, and 0.425, respectively. The difference of the three metrics with the resource diffusion algorithm based on the three-step graph is 0.25, 0.634, and 0.36. When N=50, the difference of the three metrics between this paper’s algorithm and the social diffusion algorithm based on labels is 0.197, 0.071, and 0.101.</p>
</abstract>

This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the correlation algorithm to analyze the influence of TV drama ratings and broadcasting accounted for and selecting cluster analysis to explore the relationship between TV drama broadcasting and TV type and rating. Take the ratings as the dependent variable, set the independent variables, establish the multivariate statistical model, and use SPSS software to calculate factor analysis of TV drama ratings. By combining user opinions, optimize the heterogeneous graph neural network film and television communication model based on attribute information. Test the MAE value and effect of the propagation algorithm proposed in this paper using the real Movies Lens dataset. When N=5, the recall, precision and F1 of this paper’s algorithm are 0.295, 0.751, and 0.425, respectively. The difference of the three metrics with the resource diffusion algorithm based on the three-step graph is 0.25, 0.634, and 0.36. When N=50, the difference of the three metrics between this paper’s algorithm and the social diffusion algorithm based on labels is 0.197, 0.071, and 0.101.

Exploration of Digital Communication Mechanism of Film and Television Media Industry in the Background of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the...

Exploration of Digital Communication Mechanism of Film and Television Media Industry in the Background of Artificial Intelligence

Publicado en línea: 09 dic 2023

Páginas: -

Recibido: 06 mar 2023

Aceptado: 19 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01426

Palabras clave
Association algorithms, Cluster analysis, Factor analysis, Neural networks, Digital communication

© 2023 Pan Jun, published by Sciendo

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

Exploration of Digital Communication Mechanism of Film and Television Media Industry in the Background of Artificial Intelligence

Publicado en línea: 09 dic 2023

Páginas: -

Recibido: 06 mar 2023

Aceptado: 19 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01426

Palabras claveAssociation algorithms, Cluster analysis, Factor analysis, Neural networks, Digital communication

© 2023 Pan Jun, published by Sciendo

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

Palabras clave
Association algorithms, Cluster analysis, Factor analysis, Neural networks, Digital communication