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<p>Traditional physical calligraphy education faces huge challenges and opportunities in the context of the rapid development of big data technology and the Internet. This paper first combines big data technology and the educational objectives of calligraphy and proposes the use of machine learning to solve traditional calligraphy education challenges and lead the internal development of aesthetic education in schools. Then it elaborates the research idea and structural framework of machine learning, which mainly consists of two parts: global features of the GIST algorithm and local features of SIFT algorithm, and optimizes machine learning by weighted self-learning hash high-dimensional data indexing algorithm. Finally, the study was conducted to determine the target and content of the study based on the selection of evaluation features and to use machine learning to analyze the satisfaction of calligraphy education on the decentralized Internet. A satisfaction analysis was conducted. The results showed that for students: the satisfaction value of decentralized Internet-based calligraphy education fell within the range of 35.01% to 42.98%, and its average satisfaction was 38.99%, with students outperforming parents and teachers with 38.99% average satisfaction. This study will help students to learn calligraphy systematically with half the effort so that the calligraphy culture can take root in their minds and blossom again, which is the true meaning of “learning for the past”.</p>
</abstract>

Traditional physical calligraphy education faces huge challenges and opportunities in the context of the rapid development of big data technology and the Internet. This paper first combines big data technology and the educational objectives of calligraphy and proposes the use of machine learning to solve traditional calligraphy education challenges and lead the internal development of aesthetic education in schools. Then it elaborates the research idea and structural framework of machine learning, which mainly consists of two parts: global features of the GIST algorithm and local features of SIFT algorithm, and optimizes machine learning by weighted self-learning hash high-dimensional data indexing algorithm. Finally, the study was conducted to determine the target and content of the study based on the selection of evaluation features and to use machine learning to analyze the satisfaction of calligraphy education on the decentralized Internet. A satisfaction analysis was conducted. The results showed that for students: the satisfaction value of decentralized Internet-based calligraphy education fell within the range of 35.01% to 42.98%, and its average satisfaction was 38.99%, with students outperforming parents and teachers with 38.99% average satisfaction. This study will help students to learn calligraphy systematically with half the effort so that the calligraphy culture can take root in their minds and blossom again, which is the true meaning of “learning for the past”.

Decentralized Internet-based calligraphy education leads the internal development of aesthetic education in schools

Applied Mathematics and Nonlinear Sciences

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

Traditional physical calligraphy education faces huge challenges and opportunities in the context of the rapid development of big data technology and the...

Decentralized Internet-based calligraphy education leads the internal development of aesthetic education in schools

Data publikacji: 16 sie 2023

Zakres stron: -

Otrzymano: 23 lis 2022

Przyjęty: 24 lut 2023

DOI: https://doi.org/10.2478/amns.2023.2.00197

Słowa kluczowe
Internet, Calligraphy education, Machine learning, GIST algorithm, SIFT algorithm, Hash indexing algorithm

© 2023 Chengzong Li, published by Sciendo

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

Decentralized Internet-based calligraphy education leads the internal development of aesthetic education in schools

Data publikacji: 16 sie 2023

Zakres stron: -

Otrzymano: 23 lis 2022

Przyjęty: 24 lut 2023

DOI: https://doi.org/10.2478/amns.2023.2.00197

Słowa kluczoweInternet, Calligraphy education, Machine learning, GIST algorithm, SIFT algorithm, Hash indexing algorithm

© 2023 Chengzong Li, published by Sciendo

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

Słowa kluczowe
Internet, Calligraphy education, Machine learning, GIST algorithm, SIFT algorithm, Hash indexing algorithm