摘要
随着数字媒体技术的迅猛发展，海量多媒体内容的生成与传播对高效的内容分类与识别提出了迫切需求，传统方法在面对复杂场景和大规模数据时逐渐显现出局限性。为此，本研究旨在基于深度学习技术构建一种高效的数字媒体内容分类与识别框架，以提升分类精度和系统鲁棒性。研究采用卷积神经网络（CNN）作为核心模型，并结合迁移学习策略优化模型性能，同时引入注意力机制以增强模型对关键特征的捕捉能力。此外，为解决数据不平衡问题，提出了一种自适应加权损失函数，有效提升了小样本类别识别的准确性。实验结果表明，所提出的框架在多个公开数据集上取得了优于现有方法的表现，特别是在跨领域和低质量数据场景下展现出显著优势。本研究的主要贡献在于创新性地融合了注意力机制与自适应加权策略，从而显著提高了数字媒体内容分类与识别的准确性和泛化能力，为相关领域的实际应用提供了有力支持。
关键词：数字媒体内容分类；深度学习；卷积神经网络；注意力机制；自适应加权损失函数
Abstract
With the rapid development of digital media technology, the generation and dissemination of massive multimedia content have created an urgent need for efficient content classification and recognition. Traditional methods are increasingly showing limitations when dealing with complex scenarios and large-scale data. To address this, this study aims to construct an efficient fr amework for digital media content classification and recognition based on deep learning technology, thereby improving classification accuracy and system robustness. The study employs a Convolutional Neural Network (CNN) as the core model and optimizes its performance through transfer learning strategies. Additionally, an attention mechanism is introduced to enhance the model's ability to capture key features. Furthermore, to tackle the issue of data imbalance, an adaptive weighted loss function is proposed, effectively improving the accuracy of recognition for under-represented categories. Experimental results demonstrate that the proposed fr amework outperforms existing methods on multiple public datasets, particularly exhibiting significant advantages in cross-domain and low-quality data scenarios. The primary contribution of this research lies in innovatively integrating the attention mechanism with an adaptive weighting strategy, which substantially enhances the accuracy and generalization capability of digital media content classification and recognition, providing strong support for practical applications in related fields.
Keywords：Digital Media Content Classification; Deep Learning; Convolutional Neural Network; Attention Mechanism; Adaptive Weighted Loss Function
目 录
摘要 I
Abstract II
一、绪论 1
(一) 数字媒体分类与识别的研究背景 1
(二) 深度学习在数字媒体中的应用意义 1
(三) 当前研究现状与技术挑战 2
二、深度学习模型在数字媒体分类中的应用 2
(一) 常见深度学习模型概述 2
(二) 图像分类中的卷积神经网络优化 3
(三) 文本内容分类的自然语言处理技术 3
三、数字媒体内容识别的关键技术分析 4
(一) 特征提取与表示方法研究 4
(二) 多模态数据融合的技术实现 4
(三) 数据增强与标注策略探讨 5
四、实验设计与结果分析 6
(一) 数据集构建与预处理方法 6
(二) 分类与识别算法性能评估 6
(三) 实验结果分析与改进方向 7
结 论 8
参考文献 9
