AI Topic Development Potential
AI topic development potential helps users find topics with growth momentum, recent activity, citation impact, new author entry, institutional entry, interdisciplinary fusion, and stable time-window performance. It is designed for opportunity discovery and direction planning.
Data Requirements
- Literature records with publication years and citation counts.
- AI topic labels that have been generated and reviewed.
- Author, institution, and classification data when entry and fusion indicators are needed.
Export The Index
- Confirm that the current dataset contains AI topic labels.
- Open
Index -> AI Topic Index -> AI Topic Growth Index. - Select an Excel output path.
- Export the index table and method sheet.
The exported table normally includes the composite growth index, ranking, topic scale, growth momentum, citation impact, author entry, institution entry, discipline fusion, institutional layout balance, time-window stability, document count, and recent document count.
Generate Topic Evolution
- Open
Intelligence -> AI Topic -> AI Topic Sankey. - Set the time interval, connection method, maximum topics per segment, minimum similarity, and whether only selected papers should be used.
- Generate the Sankey view.
- Save the project file for later editing, export SVG for figures, or export Excel for review.
Interpretation
A topic with high growth momentum but small scale is often an early opportunity. A topic with high scale and citation impact but weak recent growth may already be mature. High author and institution entry scores usually mean that a research community is expanding.
Use the index and the Sankey view together: the index ranks candidate directions, while the Sankey view shows whether the topic is continuous, splitting, merging, or fading.
Interface Screenshots
The screenshots below are retained to verify menu entries, parameter settings, result views, and export locations.
