## 1 Introduction  
With the rapid development of computer vision techniques, urban image data analysis has become a powerful tool for researchers, enabling the extraction of spatial features such as buildings, roads, and green spaces from satellite and street view images [1]. This advancement introduces new data dimensions and analysis methods to urban economic research, which make up for the shortcomings of traditional methods in refined and dynamic analysis. With the increasing complexity of data generated by various applications, there is an increasing need for models that can effectively analyze and interpret multimodal data such as images, text, and sensor data. Multimodal data analysis not only enhances the richness of information but also enables a more comprehensive understanding of complex scenarios, which can support decisions in several industries such as traffic management,resource allocation, and investment decisions. However, multimodal data analysis faces many challenges in fusing diverse modality information, especially when dealing with information at different scales and levels of detail. Despite the progress made in current cross-modal feature alignment techniques, it still falls short in preserving modality-specific details, which directly leads to significant information loss. Furthermore, traditional methods often struggle with challenges such as handling distribution discrepancies, scale variations, and the complexity of details across different modalities, particularly when dealing with partial data loss (e.g., incomplete modality data in image-text models). As a result, the robustness is often insufficient. Therefore, achieving high-precision alignment of multimodal data while ensuring the complete retention of information has become one of the core challenges that needs to be addressed in the field of multimodal analysis. To address the challenges outlined above, this study adopts the cutting-edge Swin Transformer V2 (denoted as SwinV2-B) [2], which demonstrates exceptional performance in hierarchical feature extraction and window-based attention mechanisms. These mechanisms enable SwinV2-B to capture both local and global patterns, making it particularly well-suited for constructing multi-scale feature representations. However, SwinV2-B shows certain limitations when handling cross-modal interactions and fine-grained multi-scale features, particularly in the fusion of cross-modal features and the complementary information exchange between modalities [3]. Recent studies have shown that incorporating multi-scale feature extraction techniques and adaptive alignment mechanisms can significantly enhance the model's robustness and expressiveness in multimodal data processing. Furthermore, existing research provides valuable insights into multimodal feature fusion, such as using scene graph analysis and cross-attention mechanisms to achieve deeper and finer interactions between modalities. However, these approaches still exhibit noticeable limitations in addressing distributional discrepancies and data loss in multimodal datasets, highlighting areas for future improvement and research potential. This paper proposes an enhanced model based on SwinV2-B, which integrates a multi-scale feature extraction module and a cross-modal attention-based feature fusion mechanism. The model is designed to dynamically integrate both local and global features while leveraging the spatial structural information from scene graphs to enhance feature representation. Specifically, the proposed method has the following notable features:  
* An innovative cross-modal alignment and fusion mechanism is introduced, addressing distributional differences in multimodal data while reducing information loss. * An advanced multi-scale feature extraction module enhances the representation of fine-grained features. * A combination of scene graph analysis and image classification enables dynamic monitoring and in-depth analysis of urban economic activities.