Abstract: Under the “Internet＋Education” context,higher education platforms have accumulated vast student behavior data,which is critical for academic early warning research.However,these data exhibit significant class imbalance.Additionally,structured student behavior data lack inherent spatial correlations among features,making it challenging for traditional deep learning methods to uncover potential feature interactions.Semantic differences among features can also lead to ineffective or misleading associations if not properly addressed,further impacting early warning accuracy.To address these challenges,a novel academic early warning model based on attention mechanisms and feature interactions is proposed.The model firstly employs a non-linear minority oversampling algorithm to augment data and mitigate class imbalance.It then uses residual connections and learnable multivariate Gaussian kernels to encode heterogeneous features into uniform vectors,reducing differences in data types and distributions.Feature interactions are modeled using a graph-based approach with semantic matching and attention mechanisms.Self-attention explores intra-sample feature relationships,while inter-sample attention captures correlations among different samples.An improved neural additive module based on Taylor formulas is introduced to provide interpretable predictions by explicitly representing model outputs as combinations of linear and non-linear feature contributions.Tensor decomposition is used to reduce computational complexity and enhance high-dimensional data processing efficiency,improving the model's generalization ability.

Key words: Academic early warning, Structured data, Feature interaction, Attention mechanism