However, these methods rely on linear relationships between data, while shield tunneling data often exhibit nonlinear relationships. Additionally, shield tunneling data are exceptionally voluminous, making conventional calculation methods unable to calculate the connections between data precisely. In recent years, due to the advancement of artificial intelligence technology, many data-driven models have been successfully applied to tunneling speed prediction. [PERSON] et al. [20], based on on-site and laboratory experiment data from a tunnel in Malaysia, proposed five different machine-learning methods for predicting tunneling speed. By comparing the predicted results of each method with actual values, they found that the K-Nearest Neighbors (K-NN) algorithm achieved the best prediction accuracy. [PERSON] et al. [21] employed the uniform extraction of samples to establish a model for predicting cutterhead torque, cutterhead thrust, and tunneling speed in the stable section. The model used a radial basis function neural network kernel and ten-fold cross-validation for the training of the Least Squares Support Vector Machine (LS-SVM) model, providing accurate predictions. Based on the Shenzhen Metro project, [PERSON] et al. [22] utilizedbackpropagation (BP) artificial neural network technology to establish a prediction model for shield tunneling parameters under complex geological conditions. [PERSON] et al. [23] introduced a novel TBM tunneling parameter prediction model that used an improved particle swarm optimization algorithm to optimize a BP neural network. The enhanced algorithm employed adaptive inertia weights, resulting in higher prediction accuracy than traditional BP and PSO-BP models. [PERSON] et al. [24], considering the temporal nature of collected TBM tunneling parameters, constructed a Long Short-Term Memory (LSTM) neural network model. Experimental results demonstrated that this model achieved optimal prediction accuracy for net tunneling speed. There is a greater abundance of research on the tunneling speed prediction of single-mode shield TBMs under different geological conditions. At the same time, there is relatively less research on the tunneling speed prediction of dual-mode shield TBMs. Dual-mode shield TBMs require the selection of different excavation modes based on varying geological conditions during the tunneling process. Simultaneously, factors such as face support balance, cutterhead tool types, and support methods may change, rendering the parameters obtained by the data acquisition system more complex. Therefore, establishing tunneling speed prediction models for dual-mode shield TBMs is inherently more complex than for single-mode shield TBMs. Furthermore, the application of machine learning algorithm models for predicting tunneling speed has been more prevalent in research. The above model data input, lack of data refinement process, data outlier processing, and its importance. In contrast, there is a relatively limited number of studies focusing on prediction models based on optimization algorithms. There is also a scarcity of research that compares the impact of different optimization algorithms on the prediction accuracy of models. Based on the left line project of Liuxiandong Station-Baimang Station Tunnel of Shenzhen Metro Line 13, this paper obtains a large number of time series characteristic parameters based on the data acquisition system, eliminates abnormal data via the isolated forest algorithm, and optimizes the original shield parameters with the improved mean filtering algorithm. Considering the influence of stratum conditions on tunneling parameters from three dimensions of surrounding rock grade, tunnel depth-span ratio, and soft-hard composite ratio, an LSTM model integrating four super-parameter optimization algorithms is established.