The complexity and diversity of traditional village landscapes present significant challenges to remote sensing image analysis. Existing methods, such as pixel-level classification, object-based image analysis (OBIA), and deep learning techniques, are often computationally intensive and require powerful hardware support and optimization algorithms. To address these issues, a landscape feature analysis model based on multiresolution feature extraction and fusion with attention pyramid decoding is proposed in this study. By employing multi-scale feature extraction and fusion, this model captures landscape features at various levels and scales, enabling more comprehensive and in-depth analysis of complex remote sensing images. Additionally, the attention pyramid decoding approach adaptively mines spatial and semantic information, enhancing the model's focus on pertinent features and consequently improving classification accuracy. Experimental results confirm the effectiveness of the proposed model for traditional village landscape analysis in remote sensing imagery.