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<p>In the context of big data development, one of the reasons for the ineffectiveness of Chinese cultural translation research is the lack of attention to the “system construction”, this paper focuses on the construction of the cultural translation research system under the big data technology, based on the MOOC recommendation system using the recommendation engine group for the hybrid recommendation, the recommendation engine group integrated the use of the mainstream collaborative filtering algorithm based on foreign translation literature in the recommendation algorithm. Through the experimental analysis, the model performance reaches the maximum when the number of cultural literature is 7, and the F_1 of the traditional method is improved by about 2.6%. Compared with the original recommendation algorithm, the improved recommendation algorithm of the MOOC implicit scoring model can get more accurate recommendation results to promote the construction of a Chinese cultural foreign translation discourse system. It is expected to promote the construction of Chinese cultural foreign translation discourse system, show the vitality, staying power, and influence of China’s excellent culture to the world, enhance the international discourse power and cultural soft power of Chinese culture in the new era, and contribute Chinese wisdom to global cultural exchange and governance.</p>
</abstract>

In the context of big data development, one of the reasons for the ineffectiveness of Chinese cultural translation research is the lack of attention to the “system construction”, this paper focuses on the construction of the cultural translation research system under the big data technology, based on the MOOC recommendation system using the recommendation engine group for the hybrid recommendation, the recommendation engine group integrated the use of the mainstream collaborative filtering algorithm based on foreign translation literature in the recommendation algorithm. Through the experimental analysis, the model performance reaches the maximum when the number of cultural literature is 7, and the F_1 of the traditional method is improved by about 2.6%. Compared with the original recommendation algorithm, the improved recommendation algorithm of the MOOC implicit scoring model can get more accurate recommendation results to promote the construction of a Chinese cultural foreign translation discourse system. It is expected to promote the construction of Chinese cultural foreign translation discourse system, show the vitality, staying power, and influence of China’s excellent culture to the world, enhance the international discourse power and cultural soft power of Chinese culture in the new era, and contribute Chinese wisdom to global cultural exchange and governance.

The Construction of Cultural Translation Research System under Big Data Technology

Applied Mathematics and Nonlinear Sciences

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

In the context of big data development, one of the reasons for the ineffectiveness of Chinese cultural translation research is the lack of attention to the...

The Construction of Cultural Translation Research System under Big Data Technology

Publié en ligne: 01 juin 2023

Pages: -

Reçu: 28 août 2022

Accepté: 07 déc. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00333

Mots clés
Big data technology, Cultural foreign translation research system, MOOC recommendation system, Recommendation algorithm, Collaborative filtering algorithm

© 2023 Min Chen, published by Sciendo

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

The Construction of Cultural Translation Research System under Big Data Technology

Publié en ligne: 01 juin 2023

Pages: -

Reçu: 28 août 2022

Accepté: 07 déc. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00333

Mots clésBig data technology, Cultural foreign translation research system, MOOC recommendation system, Recommendation algorithm, Collaborative filtering algorithm

© 2023 Min Chen, published by Sciendo

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

Mots clés
Big data technology, Cultural foreign translation research system, MOOC recommendation system, Recommendation algorithm, Collaborative filtering algorithm