How to use AI tools to analyze trends and research hotspots in different academic fields?

AI tools leverage natural language processing and machine learning to systematically analyze academic publications, enabling efficient identification of research trends and hotspots across diverse fields. This approach is highly feasible and increasingly adopted within scholarly research workflows. Effective application requires selecting appropriate AI tools (e.g., bibliometric software, semantic analysis platforms) and ensuring access to relevant, high-quality literature data sources like publication databases. Researchers must carefully define the scope (specific journals, conferences, timeframes) and chosen keywords or topics. Understanding the limitations of AI analysis, such as interpretation nuances or potential data bias, and validating findings through manual review are critical precautions to ensure reliability. To implement this, researchers typically follow key steps: 1) Select suitable AI tools (like VOSviewer, CiteSpace, or NLP libraries), 2) Collect and preprocess relevant scholarly data (articles, citations), 3) Apply techniques like topic modeling (LDA), citation network analysis, or keyword co-occurrence mapping, and 4) Interpret and visualize the results to discern emergent themes and shifts. This process significantly accelerates literature review, reveals interdisciplinary connections, and aids in strategic research planning by pinpointing rapidly evolving areas and gaps.