Recent artificial intelligence has developed rapidly with significant interdisciplinary expansion, yet existing studies often treat it as a whole, lacking systematic long-term subfield comparisons and structural analyses, thereby limiting understanding of internal differences and evolutionary mechanisms. To address this gap, we employ bibliometric methods, using expert interviews and indicator screening to construct an analytical framework. Twelve bibliometric indicators are selected across three dimensions: Impact and Dissemination, Collaboration Characteristics, and Author Characteristics. We conduct horizontal and longitudinal analyses of five subfields (AI, CV, ML, NLP, Web\&IR) from 2000 to 2024. Using CSRankings classification and a dataset of 106,622 papers, we apply violin plots, chord diagrams, and sankey diagrams to characterize structural features and evolutionary paths. Results show that these subfields have entered high-intensity knowledge diffusion: academic impact increased, knowledge dissemination accelerated, external disciplinary reliance grown, and knowledge production shifted from closed accumulation to open, interdisciplinary, multi-actor networks. On this basis, subfields exhibit significant structural differentiation: CV leads in academic impact with a task-oriented trajectory; ML shows shrinking industry collaboration but concentrated international collaboration with a relatively dispersed structure; Web\&IR is strongly industry-driven with a stable collaboration network; AI shows continuous growth; NLP remains relatively stable. Overall, this study reveals artificial intelligence evolving from unified diffusion to structural differentiation, constructs an extensible multidimensional framework, and provides a quantitative approach for understanding complex technological field evolution.

翻译：近年来人工智能发展迅速，跨学科扩展显著，然而现有研究常将其视为整体，缺乏系统性的长期子领域比较与结构分析，限制了对内部差异及演化机制的理解。为填补这一空白，我们采用文献计量方法，通过专家访谈与指标筛选构建分析框架，从影响力与传播、合作特征、作者特征三个维度选取12项文献计量指标，对2000年至2024年间的五个子领域（AI、CV、ML、NLP、Web\&IR）进行横向与纵向分析。基于CSRankings分类与106,622篇论文数据集，利用小提琴图、弦图和桑基图刻画结构特征与演化路径。结果表明，这些子领域已进入高强度知识扩散阶段：学术影响力提升、知识传播加速、对外部学科依赖增强，知识生产从封闭积累转向开放、跨学科、多主体网络。在此基础上，子领域呈现显著结构分化：CV以任务导向型轨迹引领学术影响力；ML产业合作收缩但国际合作集中，结构相对分散；Web\&IR由产业驱动明显，合作网络稳定；AI呈持续增长态势；NLP保持相对稳定。总体而言，本研究揭示了人工智能从统一扩散到结构分化的演化过程，构建了可扩展的多维分析框架，为理解复杂技术领域演化提供了量化方法。