###### Abstract
It objectively reflects the real situation and accordingly does not have comparatively strong task information, and the density of task information may be very low. So, GM data in the above study mainly needs to be 'purified' and then fed to the neural network model to meet the task requirements, and this refinement is mainly done by hand. Nevertheless, such hand-crafted features may face the following challenges in use. Namely, (a) an accurate description of the seismic response needs detailed consideration for both time domain features and frequency domain features of earthquakes. Furthermore, a hand-crafted GM feature may not always be the best. (b) Multiple factors (e.g., target task, target accuracy, etc.) must be considered when producing GM features by hand. It is also a problem to balance between feature production efficiency and the accuracy and calculation cost of the task model. Furthermore, the damage caused by earthquakes to building structures is catastrophic. Researchers from different countries have studied various aspects of vibration control problems under seismic effects [23; 24], such as active and semi-active vibration control of important building structures. Intelligent control strategies, whose core technology is to quickly capture the model parameters of controlled objects with known responses and predict the structural response at unknown moments in real time and accurately [25; 26], have more or less various defects, so the prediction of a building's structural response under ground shaking is also a very critical and challenging topic in vibration control. Considering the above observations and perspectives, designing a method that gives a suitable GM feature representation for the task at hand seems necessary, combining GM feature extraction with the target task and self-adaptively retaining the ground motion features that match the task at hand. In addition, it should also be available to improve the efficiency of the task when it is used, and achieve a balance between the seismic damage prediction method's evaluation accuracy and computational efficiency. Deep learning (DL) [27] techniques are one of the ways to achieve this goal. Indeed, Recurrent neural network (RNN) [28] architectures in DL have already been used in earthquake engineering. For example, seismic waveform classification by RNN [29; 30], earthquake location and time prediction [31], seismic trend prediction using LSTM [32], response prediction for buildings [33; 34; 35; 36], etc. Moreover, natural language is an information carrier created by humans for storing knowledge and communicating. Investigators [37] have created many successful natural language processing techniques to enable machines to obtain information in language accurately. Several methods have recently been applied in seismic/vibration feature extraction processes [38; 39; 40; 41]. Nevertheless, these feature designs are separate from the task network model; thus, the designed features may not be the best fit for the target task. Other than that, no GM feature recognition method is self-adaptive to the target task. This paper presents a time-frequency feature-based seismic response prediction model for building structures, containing two parts: an adaptive feature extraction module (AFEM) and a classification module. The proposed model uses a neural network configuration to predict the seismic state of building structures by combining AFEM with a classification network to adaptively extract time-frequency features, using ground motion records as the input that are closely related to the behaviour and response of the structure under dynamic loads. The model can also be directly embedded into a machine learning-based intelligent assessment method for efficient prediction of regional structural earthquake damage. In addition, the estimation results from neural networks with different weight constraints, different initialization methods, and different network structures and their computational costs are discussed. The study is organized as follows.