## 3 The Proposed Method
### Feature Extraction Based on RWKV, V-RRVKV and BERT Module
If they are directly concatenated, these disparities may result in an imbalance during the feature fusion process. Specifically, high-dimensional features may dominate low-dimensional features in terms of relative weight during concatenation, leading to an overemphasis on one modality during model training. This imbalance can compromise the balance and performance of multimodal learning. To address this issue, a structural transformation module is introduced in this study to uniformly map the feature vectors from different modalities, ensuring consistency in feature dimensions. Specifically, fully connected layers are employed to perform linear transformations on the extracted features, mapping them to a unified dimension. This operation enhances the balance and expressiveness of the model during multimodal feature fusion. Given that the computational complexity of fully connected layers is proportional to the product of the input and output dimensions, high-dimensional features not only increase computational cost but also significantly elevate memory and storage requirements. Therefore, in this study, image and text features are mapped to a unified 1024-dimensional feature space, which significantly reduces computational complexity and memory requirements while preserving the primary information contained in high-dimensional features. This transformation is typically implemented using a fully connected layer, as expressed in (16), where \(\mathrm{W}\in\mathbb{R}^{1024\times\mathrm{d}}\) represents the weight matrix responsible for dimensional transformation. Figure 5:2  
(5) Modal Alignment: After extracting relevant features and performing feature vector adjustment using multimodal large-scale models, the features are subjected to standardization and normalization. Given that each modality may generate features with different scales and distributions, normalization becomes a critical step to prevent any single modality from dominating the feature fusion process. For each modality, standardization is applied to ensure that the mean is zero and the standard deviation is one, thereby ensuring the comparability of features across modalities, as expressed in (17), where \(\mathrm{\mu_{modal}}\) and \(\mathrm{\sigma_{modal}}\) represent the mean and standard deviation of each modality's features, respectively. This process is applied to all modalities, including images, text, and archeological metadata, in order to create a unified feature representation that facilitates balanced fusion across modalities. Figure 5:3  
Once the features from all modalities have been standardized, they are input into the Dynamic Cross-Feature Fusion (DCFF) module described below. The DCFF module performs adaptive fusion of the three feature sets and generates modality-specific weights based on the content and relevance of each modality. This dynamic weighting mechanism ensures that each modality's contribution to the final feature representation is optimized. Through this cross-modal fusion, DCFF ensures that all available information, regardless of its origin, is appropriately integrated into a unified, high-quality representation, thereby enhancing the performance of the matching task. By applying this rigorous modality alignment approach, the model ensures that the features of each modality are comparable and that their contributions are adaptively weighted, thus optimizing the performance of the matching task.