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

<p>In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information; secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index; and finally, the data is combined with FCM (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.</p>
</abstract>

In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information; secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index; and finally, the data is combined with FCM (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.

Research on the innovation theory of college students’ ideological and political education under the background of big data

Applied Mathematics and Nonlinear Sciences

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

In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data...

Research on the innovation theory of college students’ ideological and political education under the background of big data

Data publikacji: 28 kwi 2023

Zakres stron: -

Otrzymano: 25 mar 2022

Przyjęty: 17 lip 2022

DOI: https://doi.org/10.2478/amns.2023.1.00060

Słowa kluczowe
Big data collection and analysis technology, Cloud technology education platform, FCM algorithm, Collaborative filtering algorithm, Pearson’s correlation calculation formula

© 2023 Wenling Lai, published by Sciendo

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

Research on the innovation theory of college students’ ideological and political education under the background of big data

Data publikacji: 28 kwi 2023

Zakres stron: -

Otrzymano: 25 mar 2022

Przyjęty: 17 lip 2022

DOI: https://doi.org/10.2478/amns.2023.1.00060

Słowa kluczoweBig data collection and analysis technology, Cloud technology education platform, FCM algorithm, Collaborative filtering algorithm, Pearson’s correlation calculation formula

© 2023 Wenling Lai, published by Sciendo

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

Słowa kluczowe
Big data collection and analysis technology, Cloud technology education platform, FCM algorithm, Collaborative filtering algorithm, Pearson’s correlation calculation formula