<p>Under the current background of accelerated urbanization and rural revitalization strategies, the protection and development of urban and rural landscapes have become a hot issue of social concern. This study aims to use advanced deep learning technology and generative adversarial networks to construct an efficient urban and rural landscape feature recognition model to achieve accurate analysis and classification of urban and rural landscape elements. This paper first introduces the basic principles of deep learning and generative adversarial networks and their application status in the field of urban and rural landscape feature processing and then elaborates on how to combine these two technologies to design a new urban and rural landscape feature recognition framework. The model is trained by a large number of multi-source data sets such as high-resolution satellite images and aerial photos, and successfully extracts key elements such as architectural style, green coverage, road layout, etc., and can accurately transform this information into intuitive visual expression, providing a scientific basis for subsequent urban planning. Experiments show that after training with 50,000 images, the proposed model has achieved satisfactory results: the accuracy rate of urban and rural landscape feature recognition reaches 93.2%, which is better than the current mainstream algorithms. This achievement not only helps to deepen our understanding of the spatial pattern of urban and rural areas but may also be widely used in cultural heritage protection, ecological environment monitoring, tourism development and other fields, which have important theoretical value and social significance.</p>