<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The analysis of factors influencing social responsibility in the private education industry is to promote better implementation of social responsibility in the private education industry. In this paper, a multimodal depth quantization algorithm is constructed by using a convolutional neural network and deep denoising self-coding network, and a multimodal discourse analysis model is jointly constructed based on the MDQS algorithm and multimodal semantic space. For the model constructed in this paper, quantitative analysis of the data is carried out by examples and performance evaluation experiments. From the performance evaluation, the average accuracy of the model is improved by 22.52%, 27.19% and 7.87% compared with the CCQ algorithm, SEPH algorithm and CDQ algorithm, respectively. In terms of the influencing factors, the highest frequency of the word “interest” is 20.43% and the lowest frequency of the word “culture” is 13.72%. This shows that the multimodal discourse analysis model can effectively analyze the factors influencing the fulfillment of social responsibility in the private education industry and help the private education industry to make targeted improvements to implement social responsibility.</p>
</abstract>

The analysis of factors influencing social responsibility in the private education industry is to promote better implementation of social responsibility in the private education industry. In this paper, a multimodal depth quantization algorithm is constructed by using a convolutional neural network and deep denoising self-coding network, and a multimodal discourse analysis model is jointly constructed based on the MDQS algorithm and multimodal semantic space. For the model constructed in this paper, quantitative analysis of the data is carried out by examples and performance evaluation experiments. From the performance evaluation, the average accuracy of the model is improved by 22.52%, 27.19% and 7.87% compared with the CCQ algorithm, SEPH algorithm and CDQ algorithm, respectively. In terms of the influencing factors, the highest frequency of the word “interest” is 20.43% and the lowest frequency of the word “culture” is 13.72%. This shows that the multimodal discourse analysis model can effectively analyze the factors influencing the fulfillment of social responsibility in the private education industry and help the private education industry to make targeted improvements to implement social responsibility.

Analysis of factors influencing social responsibility in private education industry based on multimodal discourse analysis model of big data

Applied Mathematics and Nonlinear Sciences

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

The analysis of factors influencing social responsibility in the private education industry is to promote better implementation of social responsibility in the...

Analysis of factors influencing social responsibility in private education industry based on multimodal discourse analysis model of big data

Pubblicato online: 30 set 2023

Pagine: -

Ricevuto: 18 dic 2022

Accettato: 10 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00394

Parole chiave
Multimodality, Deep quantization algorithm, Discourse analysis model, Denoising self-coding, Private education industry.

© 2023 Shengqing Guo, published by Sciendo

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

Analysis of factors influencing social responsibility in private education industry based on multimodal discourse analysis model of big data

Pubblicato online: 30 set 2023

Pagine: -

Ricevuto: 18 dic 2022

Accettato: 10 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00394

Parole chiaveMultimodality, Deep quantization algorithm, Discourse analysis model, Denoising self-coding, Private education industry.

© 2023 Shengqing Guo, published by Sciendo

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

Parole chiave
Multimodality, Deep quantization algorithm, Discourse analysis model, Denoising self-coding, Private education industry.