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Relying on the Hongshengsha-Yufengwei Section Tunnel Project of the second phase of Guangzhou Metro Line 7, this paper used the particle swarm optimization random forest method (PSO-RF) to establish a sealed cabin pressure prediction model in the dual-mode shield soil pressure boring mode. Through the correlation analysis of shield boring parameters, the tunneling parameters that had a great influence on the pressure of the sealing chamber were screened out, including screw speed, screw torque, cutterhead speed, propulsion speed, penetration degree, cutterhead torque, and total thrust, and the screened tunneling parameters were used as the input parameters of the prediction model, and the pressure of the sealing chamber was used as the output parameter of the model to predict the pressure of the sealing chamber. The results showed that the random forest PSO-RF prediction model improved by particle swarm algorithm could effectively predict the pressure of the dual-mode shield sealed cabin, and compared with the traditional neural network prediction model, the PSO-RF model had higher prediction accuracy, the average absolute error was within 10%, and the predicted value and the actual goodness-of-fit R2 were 0.901 4, which was significantly superior to the BP neural network in terms of prediction accuracy and generalization ability of the model. [ABSTRACT FROM AUTHOR]