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

<p>Exploring the use of red cultural design in tourism creative design is to innovate creative tourism products. In this paper, the mathematical principle of support vector machine is introduced in detail in big data, and the optimal classification discriminant function is sought using the Lagrange multiplier method. The principle of the particle swarm optimization algorithm is also explained, and the steps and flow chart of the particle swarm optimization algorithm is given. The SVM algorithm is optimized according to the particle swarm optimization algorithm, and the PSO-SVM model is constructed. Finally, the PSO-SVM model is used for index mining of the application of red tourism cultural and creative design, which is analyzed and explained from two aspects of design elements and design performance, respectively.</p>
<p>Regarding design, functional, cultural, formal, and spiritual elements account for 63.18%, 59.63%, 40.39%, and 35.99%, respectively. Regarding design performance, the percentages of symbolic refinement, imagery transformation, imitation reconstruction, and modeling isomorphism are 54.69%, 51.81%, 33.48%, and 32.75%, respectively. Based on the background of big data, the PSO-SVM model can effectively analyze the way of using red cultural and creative design in tourism cultural and creative design, focusing on highlighting the spirit and cultural connotation of red culture.</p>
</abstract>

Exploring the use of red cultural design in tourism creative design is to innovate creative tourism products. In this paper, the mathematical principle of support vector machine is introduced in detail in big data, and the optimal classification discriminant function is sought using the Lagrange multiplier method. The principle of the particle swarm optimization algorithm is also explained, and the steps and flow chart of the particle swarm optimization algorithm is given. The SVM algorithm is optimized according to the particle swarm optimization algorithm, and the PSO-SVM model is constructed. Finally, the PSO-SVM model is used for index mining of the application of red tourism cultural and creative design, which is analyzed and explained from two aspects of design elements and design performance, respectively.
Regarding design, functional, cultural, formal, and spiritual elements account for 63.18%, 59.63%, 40.39%, and 35.99%, respectively. Regarding design performance, the percentages of symbolic refinement, imagery transformation, imitation reconstruction, and modeling isomorphism are 54.69%, 51.81%, 33.48%, and 32.75%, respectively. Based on the background of big data, the PSO-SVM model can effectively analyze the way of using red cultural and creative design in tourism cultural and creative design, focusing on highlighting the spirit and cultural connotation of red culture.

The use of red cultural and creative design in tourism cultural and creative design based on the background of big data

Applied Mathematics and Nonlinear Sciences

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

Exploring the use of red cultural design in tourism creative design is to innovate creative tourism products. In this paper, the mathematical principle of...

The use of red cultural and creative design in tourism cultural and creative design based on the background of big data

Publicado en línea: 14 ago 2023

Páginas: -

Recibido: 28 oct 2022

Aceptado: 22 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00179

Palabras clave
Big data, Support vector machine, PSO-SVM model, Particle swarm optimization algorithm

© 2023 Haiyan Gu, published by Sciendo

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

The use of red cultural and creative design in tourism cultural and creative design based on the background of big data

Publicado en línea: 14 ago 2023

Páginas: -

Recibido: 28 oct 2022

Aceptado: 22 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00179

Palabras claveBig data, Support vector machine, PSO-SVM model, Particle swarm optimization algorithm

© 2023 Haiyan Gu, published by Sciendo

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

Palabras clave
Big data, Support vector machine, PSO-SVM model, Particle swarm optimization algorithm