摘要
随着数字媒体的迅猛发展，内容生产与传播模式发生了深刻变革，准确预测数字媒体内容的流行趋势成为学术界和产业界关注的重要课题。本研究以大数据技术为支撑，旨在构建一种高效、精准的流行趋势预测模型，以揭示数字媒体内容传播规律及其潜在影响因素。研究基于海量社交媒体数据，综合运用自然语言处理、机器学习及时间序列分析等方法，提取内容特征、用户行为特征以及社会环境特征，并通过深度神经网络对多源异构数据进行建模。实验结果表明，该模型在预测精度和时效性方面显著优于传统方法，能够提前识别潜在的流行内容并量化其传播潜力。此外，研究发现用户情感倾向、热点事件关联度以及社交网络结构对内容流行趋势具有重要影响。本研究的主要创新点在于首次提出了一种融合多维特征的动态预测框架，并验证了其在复杂场景下的适用性，为数字媒体内容优化、舆情监控及营销策略制定提供了理论支持和技术手段。
关键词：数字媒体内容；流行趋势预测；多维特征融合；深度神经网络；社交媒体数据
Abstract
With the rapid development of digital media, content production and dissemination models have undergone profound transformations, making the accurate prediction of popularity trends in digital media content a key topic of interest for both academia and industry. Supported by big data technologies, this study aims to construct an efficient and precise predictive model for popularity trends to uncover the dissemination patterns of digital media content and its underlying influencing factors. Based on massive social media data, the research integrates methods such as natural language processing, machine learning, and time-series analysis to extract content features, user behavior characteristics, and socio-environmental attributes. Subsequently, multi-source heterogeneous data are modeled through deep neural networks. Experimental results demonstrate that the proposed model significantly outperforms traditional approaches in terms of prediction accuracy and timeliness, enabling the early identification of potential popular content and quantification of its dissemination potential. Additionally, the study reveals that user sentiment tendencies, relevance to trending events, and social network structures play crucial roles in shaping content popularity trends. A primary innovation of this research lies in the introduction of a dynamic predictive fr amework that incorporates multidimensional features, which has been validated for its applicability in complex scenarios. This fr amework provides theoretical support and technical means for optimizing digital media content, monitoring public opinion, and formulating marketing strategies.
Keywords：Digital Media Content; Trend Forecasting; Multi-Dimensional Feature Fusion; Deep Neural Network; Social Media Data
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、大数据在数字媒体内容分析中的应用 2
(一) 大数据技术基础与框架 2
(二) 数字媒体内容的数据特征提取 3
(三) 数据驱动的内容流行趋势识别 3
三、流行趋势预测模型构建与优化 4
(一) 预测模型的理论基础 4
(二) 基于机器学习的预测算法设计 4
(三) 模型性能评估与优化策略 5
四、实证研究与案例分析 5
(一) 数据采集与预处理方法 6
(二) 典型数字媒体内容趋势预测实证 6
(三) 结果分析与改进方向探讨 7
结 论 8
参考文献 9
