Abstract: To address the limitations of existing academic performance prediction models in effectively capturing implicit student relationships,exhibiting high sensitivity to sparse data,and suffering from computational redundancy,this paper proposes a dynamically optimized prediction model based on graph isomorphism network.The model optimizes graph structure and information propagation through three core mechanisms.Firstly,a bimodal fusion graph construction technique generates dynamic adjacency matrices by integrating cosine similarity and standardized Euclidean distance,significantly enhancing relational representation robustness.Secondly,a parameter self-adaptation mechanism enables hierarchical feature fusion through learnable parameters,effectively improving adaptability to heterogeneous data.Finally,a K-value decay optimization method accelerates convergence while reducing computational complexity via progressive pruning.On the CGPA and Grade-Class datasets,the proposed model achieves prediction accuracies of 73.3% and 69.4%,outperforming optimal benchmarks including GNN by 8.3% and 4.8% respectively.The K-value decay strategy further reduces training time by 1.7 seconds compared to fixed k-value models(k=10) on Grade-Class dataset,demonstrating effective balance between computational efficiency and prediction accuracy.

Key words: Graphisomorphism network, Bimodal fusion, Academic performance prediction, Parameter adaptation, K-value decay