Utilizing the earthquake catalog from the Chinese mainland from 1970 to 2021,along with seismic sequence catalogs and historical earthquake source mechanism data,this study constructs a training and testing dataset for determining seismic sequence types. Seismic sequences are categorized into three distinct labels based on prior research:multiplet mainshocks type,mainshock-aftershock type,and isolated earthquake type. The Feature Recursive Elimination-Random Forest(RFE-RF) machine learning algorithm is employed to establish a nonlinear mapping between seismic characteristic parameters and seismic sequence types. This approach enables the early prediction of seismic sequence types at three different time nodes post-earthquake and discusses the significance of various features. The findings underscore the pivotal role of data preprocessing methods in the model's classification performance. Missing features are effectively imputed using the median value of the same sample,and the data is preprocessed using a random resampling method,yielding a high classification prediction effect. Cross-validation of the classification outcomes reveals an overall accuracy rate of 0.93 for the three types of samples one day after the earthquake. The parameters related to the main seismic source mechanism and the deviation of the P-axis azimuth from the local stress field are identified as having a greater classification contribution rate than traditional historical seismic sequence analogies at the immediate aftermath of an earthquake,i.e.,in the absence of seismic sequence data. As the post-earthquake time progresses,sequence-related features emerge as the primary determinants of the earthquake sequence type. In the seismic sequence dataset three days post-earthquake,where the maximum aftershock has occurred,the magnitude difference between the main shock and the maximum aftershock becomes a critical factor in sequence type determination. Compared to the standalone Random Forest(RF) model,the RFE-RF model demonstrates an enhanced accuracy rate of 0.41 in the test set one day after the earthquake,indicating its superior ability to distinguish between seismic sequence types.