Application and Challenge of Convolutional Neural Network in Traditional Village Ecological Landscape Design

Against the backdrop of rapid technological advancements, Convolutional Neural Networks (CNN), as a core technology in the field of deep learning, have gradually become an important tool in the ecological landscape design of traditional villages. This paper focuses on the ResNet50 model, delving into the effectiveness and challenges of CNN’s application in traditional village design. The article provides a detailed overview of CNN’s multi-level feature extraction capabilities and its exceptional performance in element recognition within traditional villages. Through specific case studies, this research demonstrates the advantages of CNN in capturing and understanding the diverse scales and layers of traditional village elements. These insights offer designers richer and more efficient tools, fostering a deep integration of traditional culture with modern technology. The article also thoroughly examines the challenges encountered in the application of CNN, such as data diversity, scene complexity, ethical privacy concerns, and real-time performance. Future research will focus on creating more diverse data sets, enhancing the model’s ability to handle complex scenarios, and ensuring data privacy and ethical compliance throughout technological development. The paper concludes that, through these balanced measures, CNN will continually explore new realms in the ecological landscape design of traditional villages, promoting an organic blend of tradition and modernity for more sustainable development.