Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology
Aiming at the problems of slow teaching resource sharing rate, long platform response time, and low student learning efficiency in traditional ideological and political learning platforms, a research on the construction of intelligent media ideological and political learning platforms based on artif...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/6511962 |
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| Summary: | Aiming at the problems of slow teaching resource sharing rate, long platform response time, and low student learning efficiency in traditional ideological and political learning platforms, a research on the construction of intelligent media ideological and political learning platforms based on artificial intelligence technology is proposed. We build an artificial intelligence open source development platform framework based on the cloud platform and use the Ceph method to optimize the storage of artificial intelligence training platform data. Under this framework, we design the business process and business module service architecture of the intelligent media ideological and political learning platform. Based on the K-means algorithm, the intelligent media ideological and political learning platform resource management module is designed, the teaching resource database is constructed, and the teaching resource sharing model component module is designed to realize the construction of the intelligent media ideological and political learning platform. The experimental results show that the sharing rate of ideological, political, and educational learning resources on the platform is relatively fast. The response time of the platform is 0.08 s when the amount of ideological and political teaching resources is 16000 MB. Students who are interested and very interested in the teaching account for 89% of the total. |
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| ISSN: | 2314-4785 |