In order to predict the impact of excavation in soft soil on existing tunnels and ensure the safe and normal operation of the tunnels, a tunnel horizontal displacement prediction model based on ANN (artificial neural network) was established to forecast the deformation caused by nearby excavations. A total of 183 sets of numerical simulation data generated from a finite element model were used as the dataset, which was randomly divided into 70% training set, 15% testing set, and 15% validation set. The model incorporated seven input parameters (including excavation width, excavation depth, and retaining wall thickness) and considered tunnel horizontal displacement as the output variable. ANN model was trained using a multilayer perceptron structure with a backpropagation algorithm, enabling analysis of the key factors affecting tunnel horizontal displacement. The results indicate that the established ANN model can accurately predict the horizontal displacement of tunnels caused by excavation, demonstrating high feasibility. ANN model proposed provides an effective tool for predicting tunnel deformation due to nearby excavation in soft soil and offers a scientific basis for risk assessment and disaster reduction decision-making in practical engineering.