## 3 The Proposed Method
### Feature Extraction Based on RWKV, V-RRVKV and BERT Module  
The input data for the feature extraction network is divided into three categories: the inscription information on the bone stick, the preprocessed bone stick images, and the archeological metadata of the bone stick (including its color, excavation context number, and missing section). The overall structure is illustrated in Figure 5. Among these, RWKV is used to extract the Chinese inscription on the bone stick. Due to the RWKV architecture's combination of the recursive properties of RNNs and the contextual modeling capabilities of Transformers, it demonstrates superior comprehension when processing Chinese text. Vision-RRVK extracts feature representations from the preprocessed bone stick images, while BERT is used to extract the archeological feature information of the bone stick. Figure 5: Architecture of the feature extraction network (RVB module). The diagram illustrates the three input branches for multimodal feature extraction: the RWKV model processes Chinese-character inscriptions from bone sticks; Vision-RRVKV takes preprocessed bone stick images as input; and BERT encodes the textual archeological metadata (e.g., color, excavation context, and missing section) in English. (1) RWKV extracts feature representations of the bone stick inscription: The RWKV feature extraction mechanism consists of the following steps. The input text is first divided into a sequence of words \(\{\mathrm{x}_{1},\mathrm{x}_{2},\cdots\mathrm{x}_{\mathrm{T}}\}\), where \(\mathrm{T}\) is the sequence length. Each word is mapped to its corresponding index in the vocabulary and transformed into an embedding vector \(\mathrm{e}_{\mathrm{R}}=\mathrm{E}_{\mathrm{R}}[\mathrm{x}_{\mathrm{t}}]\), \(\mathrm{e}_{\mathrm{R}\mathrm{t}}\in\mathbb{R}^{\mathrm{d}}\), where \(\mathrm{E}_{\mathrm{R}}\in\mathbb{R}^{\mathrm{o}\times\mathrm{d}}\) is the embedding matrix, \(\mathrm{o}\) represents the vocabulary size, where \(\mathrm{d}=768\), and \(\mathrm{d}\) denotes the embedding dimension. The core of RWKV lies in its Key-Value recurrence mechanism, designed to accumulate contextual information. At each time step, the input embedding \(\mathrm{e}_{\mathrm{t}}\) is linearly transformed into the Key and Value vectors, as shown in (2), where \(\mathrm{W}_{\mathrm{R}\mathrm{K}}\), \(\mathrm{W}_{\mathrm{R}\mathrm{V}}\in\mathbb{R}^{\mathrm{d}\times\mathrm{d}}\) are trainable weight matrices. This mechanism updates the accumulated Value \(\mathrm{s}_{\mathrm{t-1}}\) and incorporates the influence of the current time step through a dynamic weighting factor \(\mathrm{\alpha}_{\mathrm{t}}\in[0,1]\), as specified in (3). This factor balances the contributions of past states and current input. The final output hidden state \(\mathrm{h}_{\mathrm{t}}\) at the current time step is the combination of the Key and the accumulated state \(\mathrm{s}_{\mathrm{t}}\), computed as shown in (4). The RWKV model outputs the hidden state matrix \(\mathrm{H}_{\mathrm{R}}\in\mathbb{R}^{\mathrm{T}\times\mathrm{d}}\), where \(\mathrm{d}=768\). The hidden vector corresponding to the last time step \(\mathrm{h}_{\mathrm{next}}\in\mathbb{R}^{768}\) is used as the feature representation of the entire input sequence. \[\mathrm{K}_{\mathrm{t}}=\mathrm{W}_{\mathrm{R}\mathrm{e}_{\mathrm{R}\mathrm{t }}}\mathrm{V}_{\mathrm{t}}=\mathrm{W}_{\mathrm{R}\mathrm{v}}\mathrm{e}_{ \mathrm{R}} \tag{2}\]  
\[\mathrm{s}_{\mathrm{t}}=\mathrm{\alpha}_{\mathrm{t}}\times\mathrm{s}_{\mathrm{ t-1}}+(1-\mathrm{\alpha}_{\mathrm{t}})\cdot\mathrm{V}_{\mathrm{t}} \tag{3}\]  
\[\mathrm{h}_{\mathrm{t}}=\mathrm{RELU}(\mathrm{K}_{\mathrm{t}}+\mathrm{s}_{ \mathrm{t}}),\mathrm{h}_{\mathrm{t}}\in\mathbb{R}^{\mathrm{d}} \tag{4}\]  
(2) Vision-RWKV extracts bone stick image features: The feature extraction process involves patch division, Key-Value accumulation, and contextual feature generation. The input image is first divided into a series of patches with a size of \(\mathrm{P}\times\mathrm{P}\) pixels, as described in (5).