Enterprise AI Analysis
Research Progress on the Application of Artificial Intelligence in Information Engineering: A Bibliometrics Study from 2014 to 2024
In order to understand the global research trends and application development of artificial intelligence (AI) in information engineering from 2014 to 2024, this paper quantitatively analyzed and visualized the relevant literature in Web of Science (WoS) using VOSviewer and CiteSpace to identify the main authors, institutions and national collaboration networks of emerging research hotspots and cutting-edge topics. Through co-occurrence analysis and visual analysis, the research hotspots, evolutionary paths and development trends of artificial intelligence (AI) in information engineering applications were revealed, providing a theoretical basis for understanding the role of AI in information engineering and suggesting future research directions. The research found that: (1) Some stable collaborative networks dominated by small groups have been formed in this field; (2) A few countries have concentrated a large amount of scientific research resources in this field, and the global distribution of scientific research resources is unbalanced; (3) The depth and breadth of research in this field have expanded rapidly in the last decade, with research hotspots being rapidly updated and iterated, and technical application scenarios becoming more specific.
Authors: Zhenkun Liu (Jishou University), Zhu Xiao (Jishou University), Dengyuan Pan (Changsha College), Zhuo Yang (Changsha University of Science and Technology), Yuzhi Peng (Jiangnan university)
Keywords: artificial intelligence, information engineering, bibliometrics, visual analysis
Publication Date: January 10-12, 2025
Executive Impact: Key Metrics
This study on AI in Information Engineering reveals a rapidly evolving landscape. With increasing publication volume and significant collaborative networks, the field demonstrates a clear trajectory towards more specific and ethical applications.
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Total Publications (2014-2024)
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Average Citations per Paper
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Top Author Documents (Dwivedi,Y.K.)
Deep Analysis & Enterprise Applications
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Bibliometric Trends
Keyword Evolution
Analysis of publication volumes and author collaboration patterns reveals rapid growth in AI applications within information engineering, with stable, small-group collaborative networks dominating the field. The number of papers increased dramatically from 7 in 2014 to 200 in 2024, indicating a deepening research focus and continued interest.
200
Publications in 2024
Research Methodology Flow
Data Collection (WoS)
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Data Cleaning
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Bibliometric Analysis (VOSviewer/CiteSpace)
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Hotspot & Trend Identification
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Future Direction Suggestion
The research methodology involved systematic data collection, cleaning, and visual analysis to identify trends and hotspots. This structured approach ensures robust insights into the field's evolution.
| Collaboration Type | Characteristics | Impact |
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| Small-Group Networks |
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| Institutional Networks |
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| Country-Level Distribution |
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Keywords analysis reveals rapid updates in research hotspots. Early keywords like 'data mining' and 'algorithms' matured by 2019, while 'artificial intelligence learning' and 'neural networks' surged from 2017. Recent trends from 2020 emphasize 'data privacy', 'IoT', and 'data quality management', indicating a shift towards specific application scenarios and ethical considerations.
231.86
Highest Avg. Citations per Paper (Top Author)
Shifting Focus: From Fundamentals to Applications
Context: Initial research (2014-2017) focused on foundational AI techniques like data mining and algorithms. As the field matured, the emphasis shifted towards more practical applications and specific scenarios.
Outcome: Post-2017, keywords like 'artificial intelligence learning', 'neural networks', and 'driver information systems' gained prominence. Recent years (2020 onwards) show a strong focus on 'data privacy', 'IoT', and 'data quality management', reflecting the increasing maturity and real-world integration of AI.
Insight: This evolution highlights the need for continuous adaptation of research strategies to address emerging challenges and opportunities in information engineering.
Keyword Evolution Timeline
Foundational AI (2014-2017)
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Application-Specific AI (2017-2020)
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Ethical & IoT Integration (2020-2024)
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Future Research Directions
The evolution of keyword prominence shows a clear progression from core AI concepts to their practical application and ethical implications, guiding future research areas.
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Your AI Implementation Roadmap
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Phase 1: Discovery & Strategy
Conduct a deep dive into existing information workflows, identify AI opportunities, and define clear objectives and KPIs for your AI initiative. This includes data readiness assessment and technology stack evaluation.
Phase 2: Pilot & Development
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Phase 3: Scaling & Integration
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Phase 4: Optimization & Future-Proofing
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