大数据2025，Vol.11Issue(5)：67-85,19.DOI:10.11959/j.issn.2096-0271.2025056
面向教育场景的视觉大模型优化与应用
Optimization and application of vision-based large models in educational scenarios
摘要
Abstract
With the rapid advancement of artificial intelligence technology,LLMs have achieved significant success across various fields.However,their application in the field of education domain still faces challenges such as difficulties in processing multimodal data,insufficient response accuracy,and limited information delivery methods.To address these issues,a VELM was proposed.VELM was trained on multimodal public educational datasets and specialized educational datasets,and combined with model optimization techniques,VELM not only enhances response quality in educational scenarios but also optimizes and reduces computational resource consumption.Additionally,RAG technology was utilized to ensure accuracy and richness in generated content.In terms of deployment and application,VELM was implemented through the Dify platform,enabling flexible multi-end deployment,including WeChat mini programs,Web cloud platforms,and localized deployment,meeting the diverse needs of different educational scenarios.Evaluation experiments demonstrated that VELM significantly outperformed open-source large models such as MiniCPM-V,DeepSeek-VL,and Yi-VL on standard benchmark datasets like Mathvista,OCRBench,and MMMU.On specialized educational evaluation datasets,the accuracy of VELM was improved by 9.78%compared to the base model Qwen2-VL.关键词
大语言模型/多模态/智慧教育/RAG技术Key words
large language model/multimodal/smart education/RAG technology分类
信息技术与安全科学引用本文复制引用
许跃蓬,徐柴迪,郭晋军,姜云桥,王仕嘉,刘垚..面向教育场景的视觉大模型优化与应用[J].大数据,2025,11(5):67-85,19.基金项目
国家自然科学基金项目(No.42375146) （No.42375146）
国家重大科技基础设施项目(No.2024-EL-PT-000737) （No.2024-EL-PT-000737）
先进计算与智能工程国家级重点实验室基金项目(No.2023-LYJJ-01-006) （No.2023-LYJJ-01-006）
光合基金项目(No.202407013820) The National Natural Science Foundation of China(No.42375146),The National Key Scientific and Technological Infrastructure Project(No.2024-EL-PT-000737),The Fund of Laboratory for Advanced Computing and Intelligence Engineering(No.2023-LYJJ-01-006),GHfund A(No.202407013820) （No.202407013820）
