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

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.