This study aims to address the problems of incomplete temporal feature capture and low high-dimensional data visualization in mathematical education resource analysis within smart classrooms. It constructs a mathematical education resource visualization analysis model integrating the Bidirectional Long Short-Term Memory Network (BiLSTM) and Multi-Head Attention (MHA) mechanism. Taking the 16-week mathematics learning data of 1200 high school students from the smart classroom platform (including more than 150,000 behavior records, teaching resource attributes, and learning performance indicators) as the research object, this study cleans and normalizes the data, adopts Pearson correlation coefficients to screen key features, and constructs a multi-dimensional temporal matrix. Second, it uses a multi-layer BiLSTM to extract bidirectional temporal dependencies of learning behaviors, and combines MHA (number of heads = 4, single-head dimension = 32) to enhance the weights of key behaviors such as wrong-question review and delayed review. Finally, the study employs Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to achieve dimensionality reduction and visualization of high-dimensional features. Experimental comparisons with models such as the single-layer Long Short-Term Memory Network (LSTM) and BiLSTM show that the Multi-BiLSTM + MHA model achieves optimal performance. This model outperforms other models in accuracy, F1-score, and resource utilization rate, with a fast-training convergence speed. In student group clustering, the high-activity group shows significantly higher knowledge mastery levels, with good group differentiation. This model can intuitively present differences in learning patterns and has strong generalization ability. This study provides technical support for personalized mathematics teaching and precise resource allocation in smart classrooms.
Wei et al. (2026) studied this question.