<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Exploring the heritage of Chinese traditional festival culture is to enhance the national identity of the people better. This paper introduces the classification principle of support vector machines using support vector machines as an entry point in the big data perspective and shows that support vector machines are all optimized by introducing Lagrangian functions and then finding their optimal classification decision functions. Then, the principle and steps of the artificial fish swarm algorithm are introduced and explained, and the artificial fish swarm algorithm is used to optimize the support vector machine and rank the fitness values. Finally, the AFSA-SVM algorithm is used to classify and analyze two aspects of Chinese traditional festival culture inheritance, i.e., the spiritual value and inheritance mode of Chinese traditional festival culture. From the viewpoint of spiritual value, the harmonious concept of the unity of heaven and man and the self-practice of pursuing ideals are more embodied in the Lantern Festival, accounting for 32.96% and 38.09%, respectively. Regarding inheritance methods, 61.43%, 50.15%, and 55.28% of the people use the Internet, TV, and movies to inherit traditional festivals, respectively. Based on the big data perspective, the AFSA-SVM algorithm can be used to effectively classify and analyze the cultural heritage of traditional Chinese festivals, and the data can be used better to understand the spiritual connotation of traditional Chinese festivals and enhance the sense of national identity.</p>
</abstract>

Exploring the heritage of Chinese traditional festival culture is to enhance the national identity of the people better. This paper introduces the classification principle of support vector machines using support vector machines as an entry point in the big data perspective and shows that support vector machines are all optimized by introducing Lagrangian functions and then finding their optimal classification decision functions. Then, the principle and steps of the artificial fish swarm algorithm are introduced and explained, and the artificial fish swarm algorithm is used to optimize the support vector machine and rank the fitness values. Finally, the AFSA-SVM algorithm is used to classify and analyze two aspects of Chinese traditional festival culture inheritance, i.e., the spiritual value and inheritance mode of Chinese traditional festival culture. From the viewpoint of spiritual value, the harmonious concept of the unity of heaven and man and the self-practice of pursuing ideals are more embodied in the Lantern Festival, accounting for 32.96% and 38.09%, respectively. Regarding inheritance methods, 61.43%, 50.15%, and 55.28% of the people use the Internet, TV, and movies to inherit traditional festivals, respectively. Based on the big data perspective, the AFSA-SVM algorithm can be used to effectively classify and analyze the cultural heritage of traditional Chinese festivals, and the data can be used better to understand the spiritual connotation of traditional Chinese festivals and enhance the sense of national identity.

An analysis of the cultural heritage of traditional Chinese festivals based on the perspective of big data

Applied Mathematics and Nonlinear Sciences

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

Exploring the heritage of Chinese traditional festival culture is to enhance the national identity of the people better. This paper introduces the...

An analysis of the cultural heritage of traditional Chinese festivals based on the perspective of big data

Publicado en línea: 07 ago 2023

Páginas: -

Recibido: 09 oct 2022

Aceptado: 15 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00153

Palabras clave
Big data, Support vector machine, AFSA-SVM algorithm, Lagrangian function

© 2023 Zhen Li, published by Sciendo

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

An analysis of the cultural heritage of traditional Chinese festivals based on the perspective of big data

Publicado en línea: 07 ago 2023

Páginas: -

Recibido: 09 oct 2022

Aceptado: 15 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00153

Palabras claveBig data, Support vector machine, AFSA-SVM algorithm, Lagrangian function

© 2023 Zhen Li, published by Sciendo

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

Palabras clave
Big data, Support vector machine, AFSA-SVM algorithm, Lagrangian function