综采工作面矿压预测对保障煤矿安全开采具有重要意义。深度学习领域的长短时记忆网络已被证实可以提高综采工作面矿压预测的精度。然而，时间窗设置（包括历史数据长度和预测数据长度）直接影响长短时记忆网络模型的预测性能。为此研究时间窗设置对矿压预测精度的影响。首先，采用长短时记忆网络的深度学习方法对平煤股份八矿14160综采工作面矿压数据进行训练，建立矿压预测模型。其次，研究用于预测的历史数据长度对模型预测性能的影响，确定最佳历史数据长度。再者，研究在精度允许范围内的最长预测数据长度。最后，采用最佳时间窗设置，对模型的预测精度进行分析。实验结果表明，时间窗设置对预测结果有显著影响，通过优化时间窗设置可提高综采工作面矿压预测的精度。

The rock pressure prediction of the fully mechanized mining face plays an important role in ensuring the safe and efficient mining of coal mines. Long-term and short-term memory networks (LSTM) in the field of deep learning have been proven to im- prove the accuracy of mine pressure prediction at fully mechanized mining faces. However, the time window setting (including his- torical data length and predicted data length) directly affects the prediction performance of LSTM. Therefore, the influence of histori- cal data length and predicted data length on the prediction accuracy of the mine pressure is studied. First, this paper uses deep learn- ing method long-term and short-term memory network (LSTM) to analyze the historical data of the mine pressure of the 14160 com- prehensive mining face of the eighth Mine in Pingmei, and predict the future data of the mine pressure. Secondly, the influence of historical data length applied to the prediction on the prediction performance of the model is studied, and the optimal window width is determined when the prediction accuracy is the highest. Furthermore, the prediction data length that the model can be used to pre- dict the mine pressure data within the allowable range of mine pressure prediction accuracy is studied. Finally, using the optimal win- dow width, the mine pressure prediction of the fully mechanized mining face is realized within the model prediction range. The ex- perimental results show that the time window setting has a significant impact on the prediction results. The accuracy of the mine pressure prediction of the fully mechanized mining face can be improved by optimizing the time window setting.