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<p>In recent years, digital technology has rapidly penetrated various industries and brought various opportunities for developing these industries. Based on two digital technologies, BP neural network and the Gray Wolf algorithm, this article analyzes the application of digital technology in environmental art design at Xi’an Eurasian National University, taking environmental art design as an example. The article uses these two algorithms to analyze the site and design principles of the campus, followed by detailed design and consideration of the campus environment design, mainly from greenery design, groundscape and road pavement design, and campus landscape lighting design. The analysis of the collected data in this paper shows that students’ satisfaction with the central teaching area is generally low, at around 20%, and the satisfaction with the northwest teaching area is the highest, all above 40%, up to 61%. The students’ satisfaction with the plant landscape on the campus was generally at a medium level. However, the satisfaction level for loblolly pine is not very high, and overall is around 30%, while the satisfaction level for the cedar is high, and everyone’s satisfaction is higher than 60%. Therefore, this paper takes the students’ satisfaction with the campus landscape environment into consideration in the landscape design, which is conducive to achieving the perfect integration of landscape art design and ecological environment and achieving the realistic goal of both aesthetic and environmental protection.</p>
</abstract>

In recent years, digital technology has rapidly penetrated various industries and brought various opportunities for developing these industries. Based on two digital technologies, BP neural network and the Gray Wolf algorithm, this article analyzes the application of digital technology in environmental art design at Xi’an Eurasian National University, taking environmental art design as an example. The article uses these two algorithms to analyze the site and design principles of the campus, followed by detailed design and consideration of the campus environment design, mainly from greenery design, groundscape and road pavement design, and campus landscape lighting design. The analysis of the collected data in this paper shows that students’ satisfaction with the central teaching area is generally low, at around 20%, and the satisfaction with the northwest teaching area is the highest, all above 40%, up to 61%. The students’ satisfaction with the plant landscape on the campus was generally at a medium level. However, the satisfaction level for loblolly pine is not very high, and overall is around 30%, while the satisfaction level for the cedar is high, and everyone’s satisfaction is higher than 60%. Therefore, this paper takes the students’ satisfaction with the campus landscape environment into consideration in the landscape design, which is conducive to achieving the perfect integration of landscape art design and ecological environment and achieving the realistic goal of both aesthetic and environmental protection.

Application of digital technology in environmental art design

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Application of digital technology in environmental art design"}

In recent years, digital technology has rapidly penetrated various industries and brought various opportunities for developing these industries. Based on two...

Application of digital technology in environmental art design

Pubblicato online: 28 apr 2023

Pagine: -

Ricevuto: 03 giu 2022

Accettato: 14 set 2022

DOI: https://doi.org/10.2478/amns.2023.1.00091

Parole chiave
BP neural network, Grey wolf algorithm, Environmental art, Digital, Campus landscape

© 2023 Xuanyin Xu, published by Sciendo

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

Application of digital technology in environmental art design

Pubblicato online: 28 apr 2023

Pagine: -

Ricevuto: 03 giu 2022

Accettato: 14 set 2022

DOI: https://doi.org/10.2478/amns.2023.1.00091

Parole chiaveBP neural network, Grey wolf algorithm, Environmental art, Digital, Campus landscape

© 2023 Xuanyin Xu, published by Sciendo

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

Parole chiave
BP neural network, Grey wolf algorithm, Environmental art, Digital, Campus landscape