<p>The ionospheric total electron content (TEC) is significant for research related to space weather phenomena. Due to the nonlinear and highly irregular distribution of global TEC, existing forecasting models exhibit low efficiency. This study proposes a high-precision forecasting model for global TEC based on the squeeze-and-excitation (SE) attention mechanism and a combination of convolutional neural networks (CNN) and bidirectional long short-term memory (BILSTM) networks. In the data preprocessing stage, six key features are identified using the shapely additive explanations (SHAP) interpretability method, which quantifies feature contributions to TEC forecasting. The model then leverages CNN and BILSTM algorithms to thoroughly explore both long and short-term dependencies in TEC data, while the SE attention mechanism is utilized to redistribute weights to the critically influential features, enabling precise forecasting of global TEC. Forecasting experiments were conducted on global TEC, and magnetic storms were categorized based on geomagnetic indices to investigate the model's accuracy across different storm levels. The experimental results indicate that the proposed CNN-BILSTM-SE model achieves an accuracy of 3.17 TECu for TEC forecasting, significantly outperforming other models. When compared to the currently best-performing model, this new approach demonstrates a 24.3% improvement in accuracy, along with a marked reduction in training time. These findings suggest that the new CNN-BILSTM-SE model developed in this research enhances forecasting precision, shortens model training duration, and improves the overall efficiency of forecasting models. This advancement holds significant research implications and practical value for applications in space weather prediction and high-precision GNSS positioning.</p>