## 4 Experimental Environment, Dataset, and Parameters
### Dataset  
The dataset utilized in this study was provided by the Institute of Archaeology, Chinese Academy of Social Sciences. It comprises high-resolution images, inscriptions, and corresponding archeological metadata of bone sticks unearthed from the ruins of the Western Han Dynasty's Weiyang Palace in Chang'an. Before its formal use, the dataset underwent a comprehensive verification and standardization process conducted by a team of professional archeologists. Following authorized approval and with the support of funded research projects, archeological experts manually completed the annotation of matching relationships between bone stick samples, serving as the foundational data for the bone stick matching task in this study. All data annotations include ground-truth labels for bone stick fragment pairings. Before being fed into the large-scale pre-trained models, the raw image data undergoes preprocessing through the Image Preprocessing Module (IPM). Specifically, each bone stick image is first passed through the DIS-Net segmentation network to isolate and extract the core object regions. Subsequently, fragmented or damaged areas identified in the images are manually cropped and padded to enhance feature clarity. All processed images are then uniformly resized to 224 \(\times\) 224 pixels to ensure compatibility with downstream network input requirements. The associated inscription data and archeological metadata remain one-to-one aligned with the corresponding images, collectively forming a multimodal input structure that supports the bone stick matching task. The detailed procedures are as follows:  
1. Fragment Labeling: The bone stick fragments were carefully labeled by a team of professional archeologists. Each fragment was assigned a unique identifier and the corresponding inscriptions were transcribed into standardized text. The inscription data include historical content (e.g., official titles, weapon types) and spatial metadata (e.g., excavation context, missing sections). Each pair of fragments that was identified as matching was manually labeled as a matching pair. These labeled pairs serve as the ground-truth labels for the dataset used in training and testing. 2. Sampling: To ensure a diverse and representative dataset, 3000 pairs of bone stick fragments were selected. These pairs were chosen based on a wide range of fragmentation patterns, inscription types, and archeological metadata characteristics. This diverse sampling includes both well-preserved fragments with minimal damage and highly degraded fragments, ensuring that the model can generalize to different qualities of bone stick artifacts. * Preprocessing: The preprocessing of the images and metadata involved the following steps:
* Image Preprocessing: Each image was processed to remove the background and crop out the fracture regions using the DIS-Net segmentation network, which is particularly effective for high-resolution binary image segmentation. The cropped images were resized to 224 \(\times\) 224 pixels to meet the input requirements of the deep learning model. * Textual Data Preprocessing: The inscription data were cleaned and standardized, with any variations in transcription style adjusted to ensure consistency across the dataset. This allowed for the RWKV model to process the inscriptions effectively. * Archeological Metadata: Additional metadata, including details such as color, excavation context, and missing sections, were encoded using the BERT model, which helped enrich the textual features. By performing these steps, the dataset was carefully constructed to provide a high-quality foundation for training and testing the multimodal bone stick matching model. The final dataset consists of 6000 high-resolution images, each aligned with corresponding inscriptions and metadata, providing a comprehensive multimodal input for the matching task.