基于卷积神经网络的档案分类与识别技术

文中基于卷积神经网络，研究了档案分类与识别技术，旨在提高档案管理的效率和准确性。首先，介绍了卷积神经网络在图像分类任务中的应用，总结了现有研究的主要成果。然后，阐述了档案分类与识别的概念，详细介绍了卷积神经网络的基本原理和特点。接着，提出了基于卷积神经网络的档案分类与识别技术的基本流程，包括数据预处理、特征提取、分类归档等步骤。最后，详细讨论了数据采集、数据预处理、模型设计、模型训练、模型测试、结果分析等关键环节。

Archive Classification and Recognition Technology Based on Convolutional Neural Networks

Based on convolutional neural networks, this paper studies the archive classification and recognition technology, aiming to improve the efficiency and accuracy of archive management. First, the application of convolutional neural networks in image classification tasks is introduced, and the main results of existing research are summarized. Then, the concept of archive classification and recognition is expounded, and the basic principle and characteristics of convolutional neural networks are introduced in detail. Then, the basic process of archive classification and recognition technology based on convolutional neural networks is proposed, including data preprocessing, feature extraction, classification and archiving and other steps. Finally, the key links such as data collection, data preprocessing, model design, model training, model testing, and result analysis are discussed in detail.