## 2 Related Work  
In recent years, computer vision techniques have been widely applied to urban environment analysis. In [4], [PERSON] et al. employed convolutional neural networks (CNNs) to analyze satellite and street view images, enabling the automated identification of building density and land use patterns in urban environments. Compared with traditional statistical methods, image data provide finer-grained descriptions of urban characteristics. This advantage is particularly significant in rapidly developing urban areas, where image analysis can capture dynamic changes more effectively. However, their approach relies solely on CNN-based image recognition, which may fail to capture the semantic relationships between different building structures. This limitation highlights the need for integrating scene graph generation techniques to improve the understanding of urban spatial layouts, a key innovation addressed in this study. In the field of urban economic forecasting, multimodal data fusion techniques have seen remarkable progress in recent years. [PERSON] et al. proposed a model combining economic indicators with image data to predict changes in urban economic activities by leveraging spatial features from satellite images [5]. By fusing visual and traditional economic data, their method achieved higher accuracy in economic activity prediction. However, structural information within images, which is critical for understanding spatial relationships, has not been fully utilized in existing studies. By incorporating Swin Transformer and scene graph generation technologies, this study innovatively extracts spatial structural features from images, further improving the accuracy of economic activity predictions. The application of deep learning in urban image analysis has delivered significant breakthroughs. Models such as the Swin Transformer excel in processing high-resolution images due to their multi-scale feature extraction capabilities. In [6], [PERSON] et al. utilized the Swin Transformer to classify traffic congestion in urban monitoring systems, significantly improving the accuracy and real-time performance of congestion detection. Despite these advancements, the potential of combining Swin Transformer with scene graph generation for urban economic behavior recognition remains largely unexplored. This study addresses this gap by integrating these techniques to enhance analytical capabilities. Scene graph generation has emerged as a powerful tool for urban environment modeling and analysis. In [7], [PERSON] et al. proposed a method for representing urban elements such as buildings and roads in a scene graph structure, which facilitated a better understanding of spatial layouts. Graph embedding techniques further enabled these scene graphs to be used in machine learning models, advancing intelligent analysis and predictions of urban economic activities. However, existing studies have primarily focused on static analysis of structural features, without combining them with dynamic image classification for monitoring economic activities. This study bridges this gap by innovatively combining scene graph generation with the Swin Transformer to achieve bimodal fusion of urban image data and economic analysis. This approach enables real-time monitoring and prediction of economic activities while providing valuable insights for urban planning, resource allocation, and investment decisions. Scene graph generation techniques have recently been integrated with multimodal deep learning approaches for real-time urban environment monitoring. [PERSON] et al. demonstrated the efficacy of combining social media, image, and geo-information data for analyzing urban market behavior and resource allocation [8]. Building on this, this study introduces a cross-modal alignment module that leverages cross-attention mechanisms to enhance multimodal data fusion [9]. This mechanism ensures proper alignment of features from distinct modalities, improving the integration of visual and structural data.