###### Abstract  
Currently, machine learning techniques are widely used in structural seismic response studies. The developed network models for various types of seismic response provide new ways to analyse seismic hazards. However, it is not easy to balance the applicability of the input, accuracy, and computational efficiency for existing network models. In this paper, a neural network model containing an efficient self-adaptive feature extraction module (AFEM) is designed. It can recognize time-frequency features from ground motion (GM) inputs for structural seismic response prediction tasks while considering the model's computational accuracy and computational cost. The self-adaptive feature extraction module is constructed based on the MFCCs feature extraction process in NLP. AFEM recognizes time-frequency features closely related to structures' behaviour and response under dynamic loads. Taking the seismic response prediction of a typical building as the target task, the neural network configuration, including a baseline model \(M_{0}\) and three comparison models (\(M_{1}\), \(M_{2}\), and \(M_{3}\)) with AFEM, is systematically analysed. The results demonstrate that the proposed \(M_{1}\) model with initial AFEM, the \(M_{2}\) model with combined amplitude and phase features, and the \(M_{3}\) model with a complex-valued network are more adaptable than the baseline model to the target task. The extracted amplitude and phase features by the \(M_{3}\) model's AFEM significantly improve model validation accuracy by 8.6% while reducing computation time by 11.4%. It could provide the basis for future research on regional earthquake damage intelligence assessment systems. neural network; seismic response; self-adaptive feature extraction; time-frequency features 20231329  
of GM data (the long time series acceleration data). For example, [PERSON]. et al. [17] proposed a model for predicting the seismic response of multi-degrees-of-freedom structures using functionally linked neural networks (FLNN). To reduce the computational cost, the Chebyshev and Legendre polynomials were introduced in the model to process the GM data. [PERSON] et al. [18] created a real-time framework for assessing regional structural damage. This study used a long short-term memory (LSTM) neural network architecture with adjusted truncated GM data as the input to improve the stability of network training and reduce the calculation time. On the other hand, researchers used the main features of GM data extracted by hand as the input of the neural network for building the analytical model. [PERSON] et al. [19] used the ANNs technique to create a mapping between structural and ground motion characteristics with damage indices. The input part of this model adopted some main parameters that can represent ground motion characteristics: peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), duration, and dominant frequency, supplemented by some building structure parameters; the output part was an index that describes the degree of damage. [PERSON]. et al. [20] proposed a model of ANNs that can predict the damage state of R/C buildings. For the selection of network inputs, they considered the results of a previous study on the correlation between ground motion intensity measures (IMs) and structural seismic dynamic response [21]. Specifically, 14 main seismic parameters and their combinations are extracted from recorded GM data and combined with additional building structure parameters, which generate the inputs for ANNs. [PERSON] et al. [22] developed a neural network that can act as a proxy for the relationship between GM features and structural seismic responses. They extracted GM features from artificial earthquakes: significant duration, mean and predominant period, PGA, and resonance area. Essentially, GM data is a kind of ground motion signal that is captured through sensors, etc.