ResNet的大学图书馆信息素养评价系统构建  

Construction of information literacy evaluation system for university libraries on ResNet

作　　者：马骅[1] MA Hua(Xianyang Normal University Library,Xianyang 712000,Shaanxi Province,China)

机构地区：[1]咸阳师范学院图书馆,陕西咸阳712000

出　　处：《信息技术》2025年第2期136-143,共8页Information Technology

摘　　要：随着信息技术的持续发展,大学图书馆在信息服务能力方面面临着挑战。为了客观评价大学图书馆的信息服务能力,文中提出一种基于深度学习的评价模型。该研究采用残差网络进行特征提取,结合模型参数优化,以克服传统评价方法存在的主观性强、准确性不高等问题。研究结果表明,基于残差网络特征提取与模型优化的评价模型准确率达到90%,且计算效率较高。该研究为图书馆信息服务能力评价提供了一个更准确高效的技术方案,使评价结果更具客观性和参考价值。As information technology continues to evolve,University libraries face challenges in terms of information service capabilities.In order to objectively evaluate these service capabilities,this paper proposes a deep learning-based model,which adopts Residual Network(ResNet)combined with optimized the model parameters to extract feature to overcome the limitations of traditional evaluation methods and to more accurately assess the information service capabilities of university libraries.The experiment results show that the model based on ResNet characteristcs and model optimization achieves 90%,and has high computing efficiency.This study provides a more accurate and efficient technical scheme for the library information service capability evaluation,and makes the evaluation results more objective and reference value.

关 键 词：ResNet 信息服务能力 深度学习 模型训练 信息素养 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]