This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross validation (K-CV). The SCA includes two learner layers: a primary learner's layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index (TPI) and field penetration index (FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method (EM) and silhouette coefficient (Si) are employed to determine the types of geological characteristics (K) in a K-means++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics.