[目的/意义] 针对光盘音像出版物编目工作中存在的效率瓶颈，提出一种基于多模态大模型的多媒体资源编目解决方案，并以光盘音像出版物为例进行了实证研究，有助于提升编目效率，提高数据加工质量。[方法/过程] 首先整合视觉大模型和大语言模型，对光盘音像出版物外观进行识别和信息提取，然后结合FFmpeg等视频工具库，生成符合规范要求的编目数据，最后利用M3-Embedding模型将生成数据与人工数据进行对比。[结果/结论] 实验数据表明，生成的核心元数据与人工编目数据的相似度达74.3%，能有效帮助编目人员进行预编目，使工作效率得到提升。最后根据设计方案开发了一套基于多模态大模型的多媒体资源编目辅助软件，以期通过应用平台建设提高编目工作效率和准确率。
[Purpose/Significance] This study proposes a multimedia resource cataloging solution based on multimodal large models to address the efficiency bottleneck in the cataloging of CD audio and video publications. An empirical study was conducted using CD audio and video publications as an example to improve cataloging efficiency and data processing quality. [Method/Process] First,the solution integrates large vision models and large language models(LLMs) to recognize and extract information from the physical appearance of optical disc publications. It then employs video toolkits(e.g., FFmpeg) to generate standardized cataloging metadata. Finally, the M3-Embedding model is applied to compare the generated data with manual data. [Result/Conclusion] Experimental results demonstrate that the similarity between the generated core metadata and manual cataloging data reaches 74.3%, which can effectively assist catalogers in pre-cataloging and improve work efficiency. Based on this design, a multimodal large model-powered multimedia resource cataloging assistant software was developed, aiming to enhance cataloging efficiency and accuracy through platform implementation.
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