为保证煤矿安全高效开采，减少顶板事故突发。本研究提出了一种新颖的多变量长时间序列矿压预测模型——经验模态分解线性模型(EMD–Mixer)。与多数固定长度的单一特征矿压预测模型不同，该模型首先引入经验模态分解(EMD)方法将矿压信号中周期性和趋势性分离出来，再通过与长时间预测线性层(LTSF–Linear)组合，形成一个用于提取时间维度特征的模块。此外，设计了时间与通道混合策略，利用处理非线性关系的通道特征模块来处理多变量矿压数据，最后将时间与通道模块的输出使用残差与输入数据相加，得到最终的预测结果。在实验中，将历史窗口设定为36个时间单位，并对预测长度分别为24、36、48和60的不同时间单位进行了测试，结果表明，EMD–Mixer模型在短期至中长期预测范围内均表现出优异的性能和稳定性。将该模型与LTSF–Linear、MTS–Mixer模型以及矿压预测中常用的长短期记忆(LSTM)模型进行了比较，并利用4种评估指标：平均绝对误差(E_MAE)、均方误差(E_MSE)、对称平均绝对百分比误差(E_sMAPE)及绝对系数(R²)对预测结果进行了评价。结果显示，EMD–Mixer模型在所有指标下均展现出更高的预测精确度和预测稳定性。EMD–Mixer模型结构简单，具有较强的泛化能力，能够更有效地适应不同场景下不同级别的多变量矿压数据。为煤矿安全高效生产以及顶板事故的提前预警提供了重要的研究思路。

To ensure the safe and efficient extraction of coal mines and reduce sudden roof accidents, this study proposes a novel multivariate long-term sequence mine pressure prediction model—the Empirical Mode Decomposition Linear Model (EMD–Mixer). Unlike most fixed-length single-feature mine pressure prediction models, this model first introduces the Empirical Mode Decomposition (EMD) method to separate periodic and trend components from the mine pressure signals. It then combines these with the Long-Term Forecasting Linear Layer (LTSF–Linear) to form a module for extracting temporal features. Additionally, a time and channel mixing strategy is designed, utilizing a channel feature module to handle multivariate mine pressure data and process nonlinear relationships. The final prediction results are obtained by adding the residuals of the time and channel module outputs to the input data. In the experiments, the historical window was set to 36 time units, and tests were conducted for prediction lengths of 24, 36, 48, and 60 time units. The results indicate that the EMD–Mixer model exhibits excellent performance and stability across short to medium-term prediction ranges. The model was compared with the LTSF–Linear, MTS–Mixer, and the commonly used Long Short-Term Memory (LSTM) model in mine pressure prediction. Four evaluation metrics were used to assess the prediction results: Mean Absolute Error (E_MAE), Mean Squared Error (E_MSE), Symmetric Mean Absolute Percentage Error (E_sMAPE), and R-squared (R²). The results show that the EMD–Mixer model demonstrates higher prediction accuracy and stability across all metrics. The EMD–Mixer model is simple in structure, has strong generalization capabilities, and can more effectively adapt to multivariate mine pressure data of different levels in various scenarios. This provides an important research approach for the safe and efficient production of coal mines and the early warning of roof accidents.