Interdisciplinary Platform Construction Model for Rural Revitalization Talent Cultivation Based on Deep Reinforcement Learning Algorithm in the Perspective of Digital Economy–Taking Ningbo Future Rural College as an Example
Bibliographic record
Abstract
In the context of digital economy, the cultivation of rural revitalization talents urgently needs interdisciplinary collaboration and intelligent support.Taking Ningbo Future Rural College as a practice carrier, this study proposes an interdisciplinary platform construction model that integrates knowledge graph and deep reinforcement learning.The dynamic semantic association network is constructed through knowledge graph representation learning (TTransE), which is combined with a hierarchical reasoning intelligent framework to realize personalized learning and career recommendation.The experiment is based on 850 student sample data, and uses principal component analysis (PCA) dimensionality reduction with Pearson's correlation coefficient to validate feature relevance and construct a breadth-first association knowledge graph.The model achieved the highest 97.79% accuracy with 90.89% F1 value in entity recognition, and the skill assessment score was improved to 80.69, which was significantly higher than the control group's 67.10.The model significantly improved students' thinking ability (8.58 vs. 6.20),skill level (7.96 vs. 5.64), and innovation and adaptability (7.84 vs. 5.71) in this paper.The application of the proposed research methodology in Ningbo Future Rural College is effective and has certain promotion value for rural revitalization talent cultivation across specialties, which can help professional decision makers to formulate and implement cultivation policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".