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How to accurately grasp the deformation of the deep pit in the tunnel under construction, can further achieve dynamic prediction and take effective containment measures to effectively guarantee construction safety. Accordingly, a signal-based analysis method is proposed to combine the coupled empirical mode decomposition (EMD-PSO-SLFNs), bird feeding algorithm (PSO) and single cryptic feedforward neural network SLFNs learning algorithm into a multidimensional time-varying prediction model EMD-PSO-SLFNs (referred to as EPS) for pit construction under non-linear conditions. It first decomposes the EMD of the tunnel deformation in the deep pit sequence for multi-scale native modal function (IMF); and introduces IMF and PSO-SLFNS variable sequences for prediction, superimposes prediction on them, and predicts the final results using the model, and then uses the end-processing and variable sequences of the coupled PSO and SLFNs quantization algorithm for prediction. The following is an example of a tunnel pit construction in Nanning, after in-depth analysis, the relative error predicted by the EMD decomposition model alone is between 0.22% and 0.42%, with δ=0.32% actual mean difference; while the relative error predicted by the EMD-PSO-SLFNs combination for multi-dimensional time-varying decomposition model is between 0.31% and 0.75%, with δ=0.64%, the prediction accuracy is significantly higher than the former, and can be predicted under non-stable linear, variable order, providing a practical and novel method for tunnel pit deformation prediction.