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<p>In the rapid development of society, the combination of information technology and scenic environmental art design is getting closer, bringing a new creative perspective and broad space for the design field. This paper explores the integration path of information technology, regional culture and environmental art design based on this trend. It puts forward an innovative optimization strategy of ecological art design for tourist scenic spots and successfully applies it to scenic places. To verify the effectiveness of this design strategy, the article adopts BP neural network model. It combines it with genetic algorithm for deep optimization to enhance the objective evaluation of the design results. To verify the effectiveness of this design strategy, the article adopts the BP neural network model. It combines the genetic algorithm to optimize it in depth, enhancing the objectivity of evaluating the design results. After rigorous testing and analysis, the design strategy of this paper has achieved remarkable optimization effects in the lighting, ecological and cultural design of the scenic area, especially in the picturesque area of mountain diversity and forest landscape, showing a high degree of diversity, the average value reached 4.75% and 4.75% respectively. At the same time, the average value of the planting proportion of native tree species surpasses 4.6, which proves that the optimized design strategy plays a crucial role in protecting and enhancing the ecology and culture of the scenic spot.</p>
</abstract>

In the rapid development of society, the combination of information technology and scenic environmental art design is getting closer, bringing a new creative perspective and broad space for the design field. This paper explores the integration path of information technology, regional culture and environmental art design based on this trend. It puts forward an innovative optimization strategy of ecological art design for tourist scenic spots and successfully applies it to scenic places. To verify the effectiveness of this design strategy, the article adopts BP neural network model. It combines it with genetic algorithm for deep optimization to enhance the objective evaluation of the design results. To verify the effectiveness of this design strategy, the article adopts the BP neural network model. It combines the genetic algorithm to optimize it in depth, enhancing the objectivity of evaluating the design results. After rigorous testing and analysis, the design strategy of this paper has achieved remarkable optimization effects in the lighting, ecological and cultural design of the scenic area, especially in the picturesque area of mountain diversity and forest landscape, showing a high degree of diversity, the average value reached 4.75% and 4.75% respectively. At the same time, the average value of the planting proportion of native tree species surpasses 4.6, which proves that the optimized design strategy plays a crucial role in protecting and enhancing the ecology and culture of the scenic spot.

Environmental Art Design of Tourist Attractions with Regional Culture Fusion and Digital Innovation

Applied Mathematics and Nonlinear Sciences

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

In the rapid development of society, the combination of information technology and scenic environmental art design is getting closer, bringing a new creative...

Environmental Art Design of Tourist Attractions with Regional Culture Fusion and Digital Innovation

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 10 gen 2024

Accettato: 15 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0510

Parole chiave
BP neural network, Genetic algorithm, Environmental design strategy, Information technology

© 2024 Zhanfang Chen et al., published by Sciendo

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

Environmental Art Design of Tourist Attractions with Regional Culture Fusion and Digital Innovation

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 10 gen 2024

Accettato: 15 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0510

Parole chiaveBP neural network, Genetic algorithm, Environmental design strategy, Information technology

© 2024 Zhanfang Chen et al., published by Sciendo

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

Parole chiave
BP neural network, Genetic algorithm, Environmental design strategy, Information technology