AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”

Abstract

In the realm of postgraduate training within the framework of ecological civilization, accurately assessing and enhancing training programs is essential. This paper introduces AdaEcoFusion, a framework aimed at improving postgraduate education quality through hypergraph learning techniques and an adaptive ecological feature fusion model. The model categorizes and evaluates training methods by identifying peer groups of educators and mentors who share similar ecological and educational attributes. The framework addresses challenges such as the variability in training approaches across different environments and the availability of diverse ecological data. To address these challenges, AdaEcoFusion uses a hypergraph-based feature fusion that identifies high-quality ecological and educational features, reflecting each mentor’s teaching style and ecological impact. With a probabilistic model, the framework represents each mentor’s attributes in a latent space, offering a nuanced understanding of their contributions. Then we construct a large-scale graph to map relationships and similarities among educators, identifying dense subgraphs or “circles” of mentors with shared attributes. By mining these mentor circles, AdaEcoFusion enhances postgraduate training quality and provides adaptive fusion of ecological features, optimizing the training process in line with the goals of ecological civilization. Experiments conducted on a dataset of postgraduate mentors from 29 prominent universities demonstrate the model’s effectiveness in improving training programs. This underscores its capability to align postgraduate training with the objectives of ecological civilization, fostering a sustainable and adaptive educational system.

Keywords

• Adaptive ecological
• feature fusion
• hypergraph learning
• postgraduate training
• ecological civilization
• mentor circles