## 2 Basic Theory
### Model Selection  
To ensure robust feature extraction and deep semantic comprehension across heterogeneous modalities, this study adopts three pre-trained large-scale models--Vision-RMVK, RMVK, and BERT--corresponding to visual, inscriptional, and archeological features,  
Figure 2: The architecture of VRWKV: (**a**) The VRWKV framework consists of L identical VRWKV encoder layers, an average pooling layer, and a linear prediction head. (**b**) The detailed configuration of the VRWKV encoder layer is illustrated. The “Q-Shift” represents a four-way shifting approach specifically designed for visual tasks. The “Bi-WKV” module operates as a bidirectional recurrent neural network unit or a global attention mechanism. respectively--to construct a modular feature extraction architecture, in order to fully leverage each model's capacity for feature representation within its corresponding data type. For the visual modality, bone stick fracture images typically exhibit unstructured attributes such as complex boundary curvature and vulnerability to local information loss. While conventional networks excel in capturing localized textures, they often struggle to model long-range dependencies inherent in the global structure of bone sticks. The Vision-RMV model, integrating the spatial sensitivity of Convolutional Neural Networks (CNNs) with the global contextual modeling capabilities of Transformers, incorporates bidirectional attention, relative positional bias, and mechanisms for long-distance dependency modeling. These enhancements enable more efficient extraction of edge features and fine-grained texture variations in fractured regions--making it particularly effective for representing nonlinear damage patterns prevalent in bone stick imagery. In the textual modality, RWKV, as a next-generation model that synergizes the recurrent dynamics of RNNs with the contextual depth of Transformers, demonstrates superior capability in capturing long-distance semantic dependencies in Chinese text. Given that bone stick inspitions are typically devoid of punctuation, densely packed with characters, and structurally compressed, RWKV offers higher stability in modeling contextual relationships between characters. Compared to LSTM, GRU, or standard Transformer-based architectures, RWKV is better suited for handling classical Chinese and epigraphic texts in terms of semantic representation. BERT is employed to extract the archeological feature information of the bone stick, particularly for processing inscriptions. BERT is preferred over other language models like ELECTRA and RoBERTa due to its bidirectional context modeling. Unlike ELECTRA, which excels in pretraining but lacks deep contextual comprehension, BERT captures relationships between characters in dense, unpunctuated inscriptions, making it ideal for processing classical Chinese text. Additionally, RoBERTa lacks BERT's token-level pretraining on large-scale corpora, limiting its generalization ability. On the other hand, ELECTRA focuses on distinguishing replaced tokens rather than contextual understanding, which is crucial for the complex syntactical structures of bone stick inspitions. Therefore, BERT's bidirectional capability ensures high accuracy in capturing linguistic nuances and interdependencies in inscriptions, which is essential for accurate bone stick matching. RWKV, Vision-RWKV, and BERT, as representative large-scale model families that have undergone continuous iterations and optimizations in recent years, exhibit significant differences in parameter scale and applicability across various scenarios. Given that the images of bone sticks are characterized by relatively small dimensions and high complexity, to balance the overall system robustness and efficiency requirements, this study adopts the Vision-RWKV-L model, pre-trained on the ImageNet-22K dataset, to extract critical visual feature information from the fractured areas of bone sticks.