1. Bibliographic Information

• Title: 国内动漫产业研究的进展和趋势 (Research Progress and Trends in the Domestic Animation Industry)
• Authors: Yuting Chen (陈玉婷). The author is affiliated with the School of Journalism and Communication, Shanghai University, Shanghai.
• Journal/Conference: The paper was published by Hans Publishers (汉斯出版社) in a journal that appears to be titled "Journal of Communications" (新闻传播). Hans Publishers is an open-access publisher. While open-access publishing facilitates wide dissemination, the academic rigor of journals from this publisher can be a subject of debate within the scholarly community.
• Publication Year: 2024 (Published on August 20, 2024).
• Abstract: The study uses bibliometric analysis, employing tools like CiteSpace and VOSViewer, to review Chinese core journal literature on the animation industry from 2014 to 2024. It aims to identify research hotspots, trends, and potential gaps. The key finding is a downward trend in publication volume, despite industry growth, suggesting research saturation or a shift in academic focus. The paper recommends future research to focus on digital transformation, IP management, cultural integration, and business model innovation. It also points out under-researched areas like user behavior, cross-cultural dynamics, social impact, and big data-driven analysis. The ultimate goal is to provide new perspectives for academic research and foster collaboration between academia, industry, and policymakers to advance China's animation industry.
• Original Source Link: https://pdf.hanspub.org/jc2024124_323040668.pdf. The paper is formally published.

2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: Despite the significant economic and cultural growth of China's animation industry, there is a lack of a systematic, quantitative overview of the academic research landscape surrounding it. It is unclear what topics have been dominant, how research focus has evolved, and what areas remain unexplored.
  • Importance & Gaps: Understanding the trajectory of academic research is crucial for identifying mature research areas and highlighting knowledge gaps that could inform future studies, industry strategies, and policy decisions. Prior work lacked a comprehensive, data-driven analysis of the last decade's literature.
  • Innovation: This paper's fresh angle is its use of bibliometric analysis with visualization tools (CiteSpace and VOSViewer) to map the knowledge structure of Chinese animation industry research from 2014 to 2024. This provides an objective, evidence-based perspective on the field's progress and trends.

• Main Contributions / Findings (What):

  • Revealed a Declining Trend in Publications: The study found that the annual number of academic papers on the animation industry in Chinese core journals has steadily decreased from 2014 to 2024, paradoxically while the industry itself has been booming.
  • Identified Key Research Hotspots and Evolution: The analysis of keywords showed that research has evolved from early discussions on learning from Japan's model to focusing on the impact of the internet and digital technology, and more recently, to themes like IP (Intellectual Property) development, business model innovation, and the integration of traditional culture.
  • Mapped Author and Institutional Networks: The study identified influential authors who have formed a collaborative network, but found that collaboration among research institutions is sparse and underdeveloped.
  • Pinpointed Research Gaps and Future Directions: The paper concludes by suggesting unexploited research areas, including empirical studies on user behavior, the social impact of animation, detailed policy analysis, and the application of big data to industry forecasting and optimization.

3. Prerequisite Knowledge & Related Work

• Foundational Concepts:

  • Bibliometric Analysis: A research method that uses quantitative and statistical techniques to analyze academic publications (like journal articles and books). It helps identify publication trends, influential authors and institutions, key research topics, and the intellectual structure of a field.
  • CiteSpace and VOSViewer: These are specialized software tools designed for bibliometric analysis. They process publication data (authors, keywords, citations) to generate visual "knowledge maps" or networks. These maps make it easy to see connections, clusters of related topics, and trends over time.
  • Animation Industry (动漫产业): As defined in the paper, this is a comprehensive cultural industry centered on animation and comics (动漫 is a portmanteau of 动画, animation, and 漫画, comics). It includes not just the creation of content but also its distribution across various media (film, TV, web) and the commercialization of related derivative products like toys, games, and apparel.
  • IP (Intellectual Property): In this context, IP refers to a creative asset—such as a character, story, or entire fictional universe—that can be legally owned. Successful IP can be licensed and adapted into various forms (e.g., a comic book character becomes a movie, a video game, and a theme park ride), making IP development and management a central part of the modern media industry's business model.

• Previous Works & Context: The paper's "Literature Review" section (2.2) does not review other bibliometric studies. Instead, it establishes the context by describing the current state of China's domestic animation industry. Key points include:

  • Rapid Growth: The industry's total output value reached 221.2 billion yuan in 2020. The "pan-ACGN" (Animation, Comics, Game, Novel) user base is vast, reaching nearly 460 million people in 2021.
  • Government Support: The Chinese government has implemented policies like tax incentives and copyright regulations to foster the growth of the domestic industry.
  • Market Characteristics (2020-2023): The market is characterized by an expanding scale, a trend of domestic productions replacing imported ones, the formation of regional industry clusters (e.g., in Beijing, Shanghai, Guangzhou), and a competitive landscape dominated by three major factions focused on content production, distribution, and derivatives.

• Differentiation: This study distinguishes itself by providing a structured, quantitative analysis of the academic research on the industry, rather than analyzing the industry itself. While many reports describe the industry's economic status, this paper maps the intellectual conversation happening in academia, revealing what scholars have focused on and what they have neglected.

4. Methodology (Core Technology & Implementation)

The paper's methodology is a standard bibliometric analysis pipeline.

• Principles: The core principle is that the metadata of academic publications (keywords, authors, institutions, publication dates) can be analyzed quantitatively to reveal the underlying structure, evolution, and focus of a research field. By mapping these connections, one can objectively identify trends and gaps.

• Steps & Procedures:

  1. Data Collection:

     • Database: China National Knowledge Infrastructure (CNKI), a primary database for Chinese academic literature.
     • Search Strategy: An advanced search was conducted using the keywords 动漫产业 (animation industry), 动画产业 (animation industry), or 漫画产业 (comics industry).
     • Time Span: January 1, 2014, to July 2, 2024.
     • Quality Filter: The search was restricted to articles published in journals indexed by the Peking University Core Journal Directory and the Chinese Social Sciences Citation Index (CSSCI) to ensure academic quality.
     • Initial Dataset: This process yielded 463 documents.

  2. Data Processing:

     • Screening: Irrelevant documents (e.g., those focusing purely on technical animation techniques rather than the industry) were removed.
     • Standardization and Cleaning: The metadata for the remaining articles was standardized to ensure consistency. This included deduplication and correcting any errors in author names, keywords, etc.

  3. Data Analysis and Visualization:

     • Tools: CiteSpace and VOSViewer were used to conduct the analysis and generate visualizations.
     • Analysis Techniques:
       • Trend Analysis: Plotting the number of publications per year to observe research activity over time.
       • Co-occurrence Analysis: Analyzing how often authors, institutions, or keywords appear together in the literature. High co-occurrence suggests a strong relationship (e.g., collaboration between authors, or a close thematic link between keywords).
       • Cluster Analysis: Grouping keywords that frequently co-occur into thematic clusters to identify major research subfields.
       • Timeline Analysis: Visualizing when different keyword clusters emerged and evolved, showing the temporal progression of research topics.

5. Experimental Setup

This study is descriptive and analytical, so the "Experimental Setup" refers to the configuration of the bibliometric analysis.

• Dataset: The dataset consists of a curated collection of 463 academic articles on the Chinese animation industry, published between 2014 and 2024 in top-tier Chinese journals. This dataset serves as the empirical basis for the entire study.

• Evaluation Metrics: The analysis relies on several standard bibliometric indicators:

  • Publication Count (发文量): The raw number of articles published. It is used to measure the overall research activity and interest in a topic over time.
  • Frequency (出现频率): The number of times a specific item (e.g., a keyword, an author) appears in the dataset. It is a simple measure of prominence.
  • Keyword Centrality (关键词中心性):
    • Conceptual Definition: In network analysis, centrality measures the importance of a node within a network. In a keyword co-occurrence network, a keyword with high centrality acts as a crucial bridge connecting different research topics. It indicates an interdisciplinary or foundational concept.
    • Note: The paper does not provide a formula, but this is typically calculated using metrics like Betweenness Centrality in network science.

• Baselines: This type of study does not involve baselines for comparison. The results are interpreted in a descriptive manner to characterize the research field.

6. Results & Analysis

4.1. Publication Volume Analysis

该图像是图表，展示了2014年至2024年间国内动漫产业相关文献的发文数量及其逐年下降的趋势。横轴为发文年份，纵轴为发文数量，数据表明从2014年的94篇逐步减少至2024年的5篇，反映出该领域学术研究热度的逐渐减退。

As shown in Image 1, the number of publications on the Chinese animation industry shows a consistent downward trend from 2014 to 2024.

• The peak was in 2014 with 94 articles.
• The volume steadily decreased over the years, reaching only 5 articles in the first half of 2024 (data up to July 2).
• Interpretation: The author argues this decline does not reflect a failing industry, which is actually growing. Instead, it likely indicates:
  • Research Saturation: Early, foundational topics may have been thoroughly explored.
  • Shift in Research Focus: Scholars may have moved from broad "industry" studies to more niche topics, such as textual analysis of specific animated works or studies of related subcultures, which might not be captured by the search keywords.
  • Changes in funding priorities or policy environments.

4.2. Author and Institution Analysis

Author Analysis:

该图像是图表，展示了动漫产业相关文献中作者的共现关系网络。图中节点代表作者，节点大小和颜色深浅反映其发文数量和影响力，边线则表示作者间的合作关系和共现频率。整体图谱显示孟晓明、牛兴侦等作者在领域内具有较高的学术活跃度及合作密度。

The author co-occurrence network in Image 2 visualizes collaboration among researchers.

• Key Authors: The analysis identifies several prolific authors. The paper manually transcribes the top 20 authors by publication count in Table 1.

• Network Structure: A relatively close-knit network has formed around key scholars like Meng Xiaoming (孟晓明), Niu Xingzhen (牛兴侦), and the China Animation and Game Industry Annual Report Task Force (中国动漫游戏产业年度报告课题组), indicating active collaboration among the field's leading contributors.

  Below is the manually transcribed data from Table 1.

  Rank	Publication Count	First Year	Author
  1	4	2016	孟晓明 (Meng Xiaoming)
  2	3	2016	中国动漫游戏产业年度报告课题组 (China Animation and Game Industry Annual Report Task Force)
  3	3	2017	张颖露 (Zhang Yinglu)
  4	3	2016	宋磊 (Song Lei)
  5	3	2017	张立 (Zhang Li)
  6	3	2018	周焱 (Zhou Yan)
  7	2	2016	徐金龙 (Xu Jinlong)
  8	2	2017	刘华 (Liu Hua)
  9	2	2016	王承 (Wang Cheng)
  10	2	2018	左志新 (Zuo Zhixin)
  11	2	2017	张艳梅 (Zhang Yanmei)
  12	2	2018	魏扬 (Wei Yang)
  13	2	2016	刘航宇 (Liu Hangyu)
  14	2	2017	张娟 (Zhang Juan)
  15	2	2018	牛兴侦 (Niu Xingzhen)
  16	1	2018	刘雁翎 (Liu Yanling)
  17	1	2019	刘素华 (Liu Suhua)
  18	1	2017	《2016年中国动漫游戏产业年度报告》课题组 (2016 China Animation and Game Industry Annual Report Task Force)
  19	1	2016	丁粤红 (Ding Yuehong)
  20	1	2017	张琦 (Zhang Qi)

Institution Analysis:

该图像是图表，展示了国内动漫产业研究中发表文献的发文机构共现关系。图中以不同大小和颜色的圆点表示各机构，圆点大小反映机构发表文献数量，颜色代表不同的群体或研究方向。部分机构名称被标注，呈现机构间的合作或引用网络结构，但具体关系及数值未详细标出。

The institution co-occurrence network in Image 3 shows a very different pattern.

• Key Institutions: The text identifies China Animation Group, Peking University's Department of Information Management, and Huazhong Normal University's National Cultural Industry Research Center as some of the most productive institutions.

• Network Structure: The nodes are scattered and disconnected, indicating that institutional collaboration is weak and sparse. Research efforts appear to be siloed within individual universities and organizations.

  Critical Note on Table 2: The provided paper contains a significant error. Table 2, which is supposed to list the top 20 publishing institutions, is an exact duplicate of Table 1 (listing authors). Therefore, the table cannot be transcribed as intended. The textual description of top institutions is relied upon here.

4.3. Keyword Analysis

Keyword Co-occurrence and Thematic Clusters (Image 4):

该图像是图表，呈现了基于VOSviewer软件的关键词共现网络图，采用聚类上色方式展示。图中不同颜色的节点代表动漫产业研究中的多个关键词群体，节点大小反映关键词出现频率，连线表示关键词间的共现关系，揭示了国内动漫产业相关研究的热点领域和主题关联。

This VOSViewer map reveals the main thematic clusters. The central keyword is 动漫产业 (animation industry). Key connected themes are:

• Technology and Media: 数字化 (digitalization), 新媒体 (new media), 互联网 (internet).
• Business and Economics: 商业模式 (business model), 产业链 (industrial chain), 价值链 (value chain), 衍生品 (derivatives), IP.
• Cultural and Comparative Context: 文化产业 (cultural industry), 软实力 (soft power), 日本动漫 (Japanese anime), 中国动画 (Chinese animation).

Chronological Evolution of Keywords (Image 5):

该图像是关键词共现图（按时间先后上色），展示了2015年至2019年间国内动漫产业相关研究主题的关联与演变。图中圆点代表关键词，大小反映词频，颜色由蓝变黄表示时间序列，揭示动漫产业、文化产业、产业链、IP开发、大数据等研究热点及其动态关系。

This map colors keywords by the average year they appeared, showing the evolution of research focus:

• Early Period (2014-2015, Blue/Purple): Focus on foundational issues, often in comparison to Japan. Keywords include 日本动漫 (Japanese anime), 现状 (current situation), 问题 (problems), and 对策 (countermeasures).
• Middle Period (2016-2017, Green): Shift towards the impact of technology and new business structures. Keywords include 互联网 (internet), 大数据 (big data), 商业模式 (business model), and 盈利模式 (profit model).
• Recent Period (2018-2019, Yellow): Concentration on higher-level strategic concepts. Keywords include IP, 游戏 (gaming), and 传统文化 (traditional culture), reflecting a focus on intellectual property monetization and cultural branding.

Keyword Hotspots (Image 6):

该图像是关键词共现图（按热度上色），展示了动漫产业相关研究中的高频关键词分布。其中“动漫产业”“动漫”“文化产业”等关键词显著突出，反映出研究热点集中于产业链、文化融合、技术创新及商业模式等领域。整个图谱通过颜色深浅显示关键词的流行程度，帮助揭示国内动漫产业研究的重点方向和趋势。

The heatmap visualization reinforces the centrality of key terms. The brightest spots are 动漫产业 (animation industry), 文化产业 (cultural industry), and 产业链 (industrial chain), confirming these as the core and most frequently discussed topics in the field.

Top 20 Keywords by Frequency (Table 3): This table provides a quantitative ranking of the most important keywords.

Keyword Clustering and Timeline Analysis (Images 7 & 8):

该图像是关键词聚类图，展示了动漫产业研究中的十大主题群，包括“动漫产业”“文化产业”“产业链”“IP”“创新”等，色彩区分不同主题簇，反映了各关键词间的关联和研究热点分布，揭示了该领域的研究结构与重点。

该图像是图表，呈现了国内动漫产业研究关键词的聚类时间轴。图中通过不同颜色和编号（0至9）区分十大主题方向，如动漫产业、文化产业、产业链、IP及创新等，反映各领域关键词在2014年至2024年的动态演变和研究热点变化。图表直观展示了各聚类间的时间联系和关键词发展趋势。

CiteSpace analysis generated 10 major keyword clusters, representing the main research themes:

• #0 动漫产业 (Animation Industry)

• #1 文化产业 (Cultural Industry)

• #2 产业链 (Industrial Chain)

• #3 内容产品与商业模式 (Content Products & Business Models)

• #4 发展困境与出路 (Development Dilemmas & Solutions)

• #5 IP

• #6 创新 (Innovation)

• #7 产业转型 (Industrial Transformation)

• #8 人才培养 (Talent Cultivation)

• #9 量化方法 (Quantitative Methods)

  The timeline view (Image 8) maps these clusters over the 2014-2024 period, visually confirming the thematic evolution from foundational industry analysis towards more specific topics like IP, innovation, and industrial transformation in later years.

7. Conclusion & Reflections

• Conclusion Summary: The paper successfully uses bibliometric methods to map the landscape of Chinese animation industry research over the past decade. It identifies a surprising decline in academic publication volume against a backdrop of strong industry growth. The research focus has matured, shifting from foundational problem/solution analyses and comparisons with Japan towards more sophisticated topics like IP management, digital transformation, and cultural integration. While author collaboration exists among top scholars, institutional collaboration is notably lacking.

• Limitations & Future Work (as identified by the author):

  • Future Trends: Research should continue to explore:
    • Digital Transformation: The role of AI, AIGC (AI-Generated Content), VR, and digital twins.
    • IP Management: Strategies for cross-media IP development and monetization.
    • Cultural Fusion: Integrating traditional Chinese culture to enhance market appeal and soft power.
    • Business Model Innovation: Exploring new revenue streams in a changing market.
    • Industrial Chain Optimization: Improving synergy between different sectors of the industry.
  • Identified Research Gaps: The author suggests several underexplored areas for future scholarship:
    • User Behavior and Psychology: In-depth studies of consumer habits and preferences.
    • Cross-Industry Interaction: Research on the convergence of animation with film, gaming, and music.
    • Social Impact: Empirical studies on how animation influences social values, especially among youth.
    • Policy Impact Analysis: Rigorous evaluation of how government policies have affected the industry.
    • Big Data Analytics: Using data mining and machine learning for predictive analysis and industry optimization.

• Personal Insights & Critique:

  • Strengths: The study provides a valuable and methodologically sound overview of its chosen topic. The visualization of trends and research clusters offers a clear and intuitive understanding of the field's evolution. The identification of a decline in research output is a provocative and important finding.
  • Weaknesses & Critique:
    1. Critical Error: The duplication of Table 1 as Table 2 is a major flaw that undermines the paper's credibility and suggests a lack of careful editing.
    2. Publisher Reputation: The paper is published in a venue that may not be considered top-tier in all academic circles, which could affect how its findings are perceived.
    3. Speculative Interpretation: The explanation for the decline in publications (saturation, shift in focus) is plausible but remains an interpretation. Other factors, such as changing journal acceptance criteria or a shift in academic prestige away from broad industry studies, could also be at play.
  • Overall Impact: Despite its flaws, the paper serves as a useful meta-analysis for researchers and students entering the field of Chinese animation studies. Its findings on thematic evolution align well with observable trends in the global media landscape, and its proposed future directions provide a practical roadmap for generating impactful new research. The paradox of a booming industry and waning academic attention is a key takeaway that warrants further investigation.