## 1 Introduction
Our study, however, overcomes this limitation by integrating multiple modalities--image data, inscriptions, and archeological metadata, which enables a more holistic understanding of bone stick fragments, improving matching precision and accuracy. * Inadequate image preprocessing: Many traditional methods fail to perform critical preprocessing tasks such as denoising, enhancement, or structural normalization, which are essential to prepare the input data for effective feature extraction. As a result, these methods often suffer from high computational burden and reduced model performance during feature extraction and task modeling. In contrast, our method introduces a more robust preprocessing pipeline, which precisely segments fracture regions and minimizes noise interference, thus allowing the model to focus on key features and significantly improving matching efficiency. * Insufficient robustness in matching algorithms: The majority of existing algorithms struggle when dealing with highly variable fragment morphologies and severe surface degradation, resulting in weak feature extraction capabilities. As a result, the accuracy of recognition and reconstruction in such conditions is significantly diminished, greatly limiting the generalizability and practical applicability of these methods. Our approach, however, leverages advanced pre-trained models like Vision-RWCV, RWCV, and BERT, which are specifically designed to extract deep features across multiple modalities, ensuring that our model is far more robust to image degradation, missing fragments, and complex geometric variations. This multi-faceted feature extraction combined with a dynamic fusion mechanism enhances the generalization capability, making our method more applicable to a wide range of real-world scenarios. To address the aforementioned challenges, this paper introduces a pre-trained large model-driven multimodal information fusion matching method. By extracting multimodal feature information, the model's representational capacity during the feature extraction process is enhanced. Furthermore, through a dynamic cross-feature fusion strategy, nonlinear interactions between features are modeled, which not only improves matching accuracy but also enhances the model's generalization capability. This approach integrates three types of information--image data from the fracture surfaces of the bone sticks, the inscriptions on the bone sticks, and archeological information about bone sticks (including color, excavation context number, and missing section)--as input to the model. Initially, pre-trained large-scale models, namely Vision-RWCV [8], RWKV [9], and BERT [10], are employed to extract features from different modalities of data. Then, a dynamic cross-modal feature fusion module is used to effectively combine the various features. Finally, bone stick image matching is achieved by calculating feature similarities. A contrastive learning approach is utilized, and through transfer learning, the pre-trained weights of the large-scale models are used to initialize the bone stick matching task, thereby accelerating the model's adaptation to this specific domain. The primary contributions of this paper are as follows:  
* Design and implementation of the multimodal matching method: To ensure the comprehensive extraction of various information features while maintaining the overall robustness of the system and the operational efficiency of the model, this paper proposes an integrated matching system driven in parallel by three large models. The aim is to enhance the feature extraction capability in order to fully exploit the latent information within the data of bone sticks. By leveraging different large models corresponding to the distinct characteristics of the data, feature extraction is carried out separately for each type of data, thereby providing a more precise basis for bone stick image matching.