基于深度学习的教师资源管理平台智能化应用研究
Research on Intelligent Application of Teacher Resource Management Platform Based on Deep Learning

本文首先介绍了大数据和深度学习的关系，深度学习是大数据技术实现的重要方法，然后分析了大数据技术现阶段在基础教育中的应用情况，最后介绍了深度学习在教师档案管理中的图像分类应用，阐述了图像深度学习技术不仅可以分析图像视频，还可以实现图片快速检索和分类，在实践中，卷积神经网络在图像中应用的成功案例较多，本文选择了卷积神经网络的LeNet模型，完成对教师档案图片管理中的图像分类功能。深度学习技术可以实现对教师档案中的图像按照预定要求的分类，在一定程度上说明了深度学习可以合理地应用到基础教育行业中，也说明了大数据技术应用到基础教育行业的可行性，能够为基础教育提供预测分析和决策支持，为培养高素质教师队伍，推进教育数字化提供强有力的支撑。
This article begins by explaining the relationship between big data and deep learning, emphasizing that deep learning is a crucial method for implementing big data technology. It then examines the current application of big data technology in basic education. Lastly, the article explores the use of deep learning in image classification for teacher archives management. It details how deep learning technology can analyze images and videos, as well as facilitate rapid image retrieval and classification. In practice, convolutional neural networks (CNNs) have been successfully applied in various image-related cases. For this article, the LeNet model of CNNs was chosen to perform image classification tasks within teacher archives. The ability of deep learning technology to categorize images within teacher archives according to specific requirements demonstrates its viable application in the basic education sector. This also highlights the feasibility of applying big data technology to basic education, offering predictive analysis and decision-making support. Such applications are instrumental in fostering a high-quality teaching workforce and advancing the digitalization of education, providing robust support for these initiatives.