Abstract: Long-period ground motions (LPGM), due to their abundant low-frequency components, are prone to causing earthquake damage to long-period structures such as super-high-rise buildings, long-span bridges, and large-scale lifeline engineerings, based on different formation mechanisms and characteristics, they can be divided into near-field long-period ground motions and far-field long-period ground motions. At present, there is no unified standard for the definition of LPGM, existing methods are mostly based on a single parameter, which cannot characterise the complex characteristics of LPGM and lack definition for different types of LPGM. In view of this, the PCA-SVM model and the CNN-LSTM-Attention model based on machine learning and deep learning respectively from two perspectives of manual multi-angle feature extraction and automatic picking up of spatio-temporal joint features to construct the near- and far-field long-period ground motion recognition was built in this study, which finally achieved an accuracy of 96.01% and 97.83% in the test set respectively. The results show that the two classification models proposed in this study have good prediction effects on near-field and far-field long-period ground motions, and have superiority compared with the traditional methods, which can provide a reference for the identification and selection of LPGM.