The large-scale invasion of Spartina alterniflora has endangered the ecological security of China's coastal area.Therefore,studying a fast and accurate algorithm for identifying Spartina alterniflora is particularly important for achieving dynamic monitoring within the region.Taking Xiangshan harbor as a research zone in this study,151 Spartina alterniflora and 140 non-Spartina alterniflora land patches were used as the training data set on the GEE platform.The index of NDVI,EVI,NDWI and BSI were extracted from the Sentinel-2 remote sensing image band information,and these indices were added to the remote sensing image data.Machine learning methods such as Support Vector Machines and Random Forests were used for identification and classification.By identifying and classifying Sentinel-2 remote sensing images from 2017 to 2022,dynamic monitoring of Spartina alterniflora within the study area was achieved.The research results showed that compared with SVM,the RF method had higher recognition accuracy for identifying Spartina alterniflora,and the overall recognition accuracy in 2022 reached 99.03% with a Kappa coefficient of 0.978 7.At the same time,the experimental results showed that the area of Spartina alterniflora in Xiangshan harbor has gradually decreased since 2017,indicating that the artificial intervention measures taken during this period were very effective.The dynamic monitoring and status analysis of Spartina alterniflora in Xiangshan harbor provided quantitative scientific data for the management of Spartina alterniflora,and have important reference value for formulating relevant prevention and control measures.