掘进参数的有效预测是盾构智能化提升的关键，为建立软硬复合地层下盾构掘进参数的预测模型，提出了地质状况的量化方法。依托济南地铁R1、R2、R3线软硬复合地层下的盾构隧道数据，通过将隧道以及上下方各一个开挖面直径内的地层沿着埋深单元化，分单元统计地层特征参数，建立了地质状况的量化矩阵，进一步基于卷积神经网络自动提取地质量化矩阵的特征，最终实现了掘进参数的预测。研究结果表明： 1）基于地质量化矩阵建立的掘进参数预测模型在训练集、验证集、测试上表现良好； 2）将模型应用于新的地质状况类似的盾构隧道上，刀盘转矩、总推进力、推进速度、刀盘转速的预测值与实际值的变化趋势较为一致，且平均误差在15%之内，预测精度可满足工程需求； 3）对比目前基于加权的地质参数建立的BP神经网络模型，掘进参数的预测精度和稳定性提升明显。

The effective prediction of tunneling parameters is the key to the intelligent improrement of shield. In order to establish the prediction model of shield tunneling parameters under soft and hard mixed ground, a quantitative method of geological conditions is proposed. Relying on the shield tunnel data of Jinan metro line R1, R2, and R3 under soft and hard mixed ground, the quantitative matrix of geological conditions is established by unitizing the stratum within the diameter of the tunnel and the upper and lower excavation faces along the buried depth, and counting the stratum characteristic parameters by units. Further, the characteristics of the geological quantitative matrix are automatically extracted based on convolutional neural network, and the prediction of tunneling parameters are finally realized. The results show that: （1）The prediction model of tunneling parameters based on geological quantification matrix performs well in training set, verification set, and test set. （2）When the model is applied to a new shield tunnel with similar geological conditions, the predicted values of cutter head torque, total propulsion force, propulsion speed and cutterhead speed are consistent with the actual values, and the average error is within 15%. （3）The prediction accuracy can meet the engineering requirements. Compared with the current BP neural network model based on weighted geological parameters, the prediction accuracy and stability of tunneling parameters have been significantly improved.