The accurate real-time prediction of complex time series in airport runway settlement is crucial. In order to achieve precise and reliable predictions of runway surface settlement, considering the nonlinear and time-dependent characteristics of settlement data under the coupling effects of dynamic loads, water levels, and other factors, a CNN-BiLSTM-Attention approach is proposed. This method first employs Convolutional Neural Networks (CNN) to extensively extract local temporal features from the input sequences. Subsequently, the extracted local temporal features are input to BiLSTM layers to learn the features of the sequences both in the forward and reverse directions. Finally, the output of the BiLSTM layers is fed into an attention mechanism layer. By computing attention weights, crucial information in the input sequences is weighted and added to enhance the model’s focus on key information. The final settlement prediction is then generated through a fully connected layer. To validate the proposed method, field settlement monitoring data from Runway L at Hangzhou Xiaoshan Airport are utilized as an engineering case study. The results indicate that the combined model captures local features of settlement sequences more effectively with a smaller dataset, achieving higher accuracy compared to a standalone BiLSTM model.