Habitats and ecosystems are seriously affected by the spread of Spartina alterniflora in mangrove wetlands. To date, S. alterniflora has been a focus of biological invasion research. However, current research results on the dynamics of landscape patterns and its driving forces remain rare. In this article, remote sensing images in 2009, 2013 and 2019 were taken as data sources and the methods of man-machine translation and field exploration were used to identify the layout of S. alterniflora in the Shankou Mangrove Reserve. At the same time, changes in diffusion characteristics and the driving forces behind them were analysed by means of a land type transfer matrix, centroid variation, landscape index analysis and grey correlation analysis. The results showed that (1) the area and number of patches of S. alterniflora increased from 2009 to 2019. But the growth rate of the area covered by S. alterniflora decreased, the average annual growth rate was 7.60% during 2009-2013, and 1.99% during 2009-2019. The average annual growth rate of the area covered by S. alterniflora was higher than that covered by mangrove. The area of mudflat transformed into S. alterniflora was 1.507 times that of mudflat transformed into mangrove. Both S. alterniflora and mangrove displayed a fragmentation trend. (2)The centroid coordinates of S. alterniflora were located in Dandou tidal flat during 2009-2019.The patch centroid of S. alterniflora shifted to the northwest during 2009-2013 and shifted to the southeast during 2013-2019. (3) The dynamic change of S. alterniflora was affected by both human and natural factors during 2009-2019. The proportion of the sea-going population was the most closely correlated with the maximum patch index, patch density and patch number of S. alterniflora. The annual maximum temperature had the highest correlation with the fractal dimension, fragmentation index and area covered by S. alterniflora. (4) The change in the area covered by S. alterniflora was mainly driven by climate change and human factors. The factors influencing the area covered by S. alterniflora were the lowest annual temperature, the highest annual temperature, the proportion of the sea-going population, and the GDP. The area covered by S. alternifolia was positively correlated with the highest annual average temperature and the lowest annual average low temperature. Also, the area was negatively correlated with the proportion of the sea-going population. It is hoped that this work will provide a valuable reference for the monitoring of S. alterniflora as well as a theoretical basis for mangrove protection.