## 1 Introduction  
A substantial number of fragmented bone stick artifacts have been unearthed at the site of the Weiyang Palace in Chang'an City during the Han Dynasty. These bone sticks, made from animal scapulae, are long, bar-shaped objects, with over 57,000 of them inscribed with textual information [1]. The inscriptions on these bone sticks encompass a diverse array of subjects, spanning official titles, categories of weapons, and units of measurement throughout the Western Han period. They vividly reconstruct the social structure and development of the Western Han Dynasty [2], offering profound academic and cultural value to archeological research. However, due to prolonged burial, the bone sticks have been subjected to the erosive forces of soil movement and their inherent structural degradation. As a result, most bone sticks are found broken into upper and lower fragments, often already fractured by the time of excavation. Archeologists primarily rely on observing the texture of the fracture surfaces and the inscription to match fragments. This process is highly time-consuming and labor-intensive, with the risk of causing secondary damage to the bone sticks during the matching process. The application of digital image processing techniques for the matching of bone stick fragments presents a promising strategy to substantially improve the efficiency and accuracy of the matching process. In current research within the domain of image matching, considerable progress has been made. For instance, [PERSON] et al. [3] proposed an image matching method based on Freeman coding. Freeman coding is highly effective in representing regular boundaries; however, it exhibits clear limitations when dealing with fragments that have irregular shapes or complex boundary curvature, resulting in a significant reduction in generalization capability during matching. [PERSON] et al. [4] introduced a matching method based on Siamese networks, which relies on the complete geometric and textural information of fracture surfaces. However, when fracture surfaces are severely degraded due to corrosion, missing sections, or noise, the accuracy of feature extraction and matching based on individual point clouds is substantially diminished. [PERSON] et al. [5] designed a multi-feature matching method based on residual networks and addressed the multi-fragment stitching problem through a dynamic matching algorithm. Nonetheless, when the geometric edges of fragments are severely damaged, extracting valid feature points from images becomes exceedingly difficult, thus reducing matching accuracy. [PERSON] et al. [6] proposed an automatic matching algorithm based on feature region partitioning, which primarily depends on the geometric shape features of the fragments. However, this method overlooks other potentially valuable information, resulting in lower accuracy for fragments with sparse surface features or unclear boundary characteristics. [PERSON] et al. [7] presented a stitching method based on fracture surface profile features, integrating curvature and neighborhood information. This approach uses geometric matching and iterative optimization algorithms to achieve fragment registration and stitching. However, for fragments with nonlinear surface textures or multi-layered complex structures, the extraction and matching of feature points significantly deteriorate, thereby affecting the overall accuracy of the matching process. Although the aforementioned methods demonstrate a certain degree of effectiveness under specific conditions, they continue to face several critical bottlenecks in practical applications, which this study effectively addresses:  
* Limited Modality Integration: Existing approaches heavily rely on raw image data, failing to incorporate semantic information that could provide a more comprehensive understanding of the fragments. The lack of integration between high-level semantics and low-level visual features results in suboptimal information utilization, significantly impairing model performance.