###### Abstract
The decomposed intrinsic mode functions (IMFs) component sequences exhibit strong regularity. Recently, the application of machine learning algorithms such as Long Short-Term Memory (LSTM) [14; 15], Convolution Neural Networks (CNN) [16; 17; 18], and Random Forest (RF) [19; 20] has also become increasingly popular in the field of Structural deformation prediction. This is due to its superior forecasting ability in dealing with complex nonlinear data [21] and its capability to effectively capture information in time series data [22]. [PERSON] et al. [23] proposed a concrete dam deformation prediction method based on the attention mechanism, where the attention mechanism filters information that significantly affects deformation. Coupled with the computationally efficient and memory-efficient Adam optimization algorithm, it enhances the learning accuracy and speed of LSTM. [PERSON] et al. [24] proposed a Convolutional Long Short-Term Memory (ConvLSTM) network aimed at learning and extracting spatiotemporal latent features from concrete bridge strain response prediction data. [PERSON] et al. [25] proposed a neural LSTM model for predicting reservoir water levels and dam deformations, utilizing the combination of InSAR and LSTM to predict dam failures by adjusting reservoir water levels. [PERSON], [PERSON] et al. [26] propose an approach leveraging 1-D CNN for structural health monitoring, accurately identifying cracks in concrete (95.24% accuracy) from raw EMI signatures, thereby enhancing efficiency and reliability. [PERSON] et al. [27] proposed a novel deep learning approach based on one-dimensional CNNs that utilizes EMA signals to automatically identify tiny damages in concrete, offering a new perspective for damage identification in infrastructure. [PERSON] et al. [28] proposed a Dual Attention Mechanism CNN-LSTM network model (DACLnet) for precise monitoring and accurate prediction of nonlinear surface deformation, this method can provide important technical support for early warning and prevention of geological disasters. [PERSON] et al. [29] proposed an enhanced RF model integrated with a sliding time window strategy for predicting dam deformation. This approach effectively handles nonlinear data and outliers, enhancing prediction accuracy and robustness while simplifying model structure, making it a suitable tool for practical monitoring tasks. However, due to the inherent nonlinearity and non-stationarity of monitoring sequences, a single prediction model often fails to achieve ideal prediction accuracy [30; 31]. Therefore, many researchers have proposed hybrid methods combining artificial intelligence techniques with data preprocessing strategies. Among these, decomposition-based methods are widely adopted as data preprocessing techniques, including EMD [32; 33; 34],wavelet decomposition (DWT) [35], EEMD [36], and complementary ensemble empirical mode decomposition (CEEMD) [37]. These methods offer effective means to address the complexity and non-stationarity of monitoring data. [PERSON] et al. [38] proposed an EMD-LSTM model for long-term deformation prediction of mining dams combined with an attention mechanism. [PERSON] et al. [39] proposed an EEMD-RNN model for dam deformation prediction. Experimental results clearly indicate that, compared to single models and traditional signal processing techniques, decomposition-based hybrid prediction methods exhibit significant advantages in terms of prediction performance [40; 41]. However, these deformation prediction models typically train and predict directly on the decomposed sequences, which can lead to high computational costs and insufficient training samples due to the large number of decomposed sequences [42]. Additionally, directly training models with various monitoring data can pose challenges in feature learning and result in lower prediction accuracy. Therefore, to enhance training efficiency and prediction accuracy, it is necessary to further analyze the spectrum of the decomposed monitoring data and cluster the data with similar characteristics.