As global temperature rises and international trade becomes more frequent, the world is facing increasing pressure from invasive species. Therefore, it is crucial to have a deep understanding of the types of invasive species and the possible distribution ranges to take effective prevention and control measures. This paper reported a new record invasive plant species in Hainan, Acmella uliginosa, and MaxEnt maximum entropy ecological niche modeling combined with Geographic Information Systems (GIS) was used to predict the primary potential suitable areas for A. uliginosa under current and future climatic conditions in China, ROC curve analysis method was used to validate, knife-edge method to was used to analyze the major environmental variables influencing the distribution of the A. uliginosa. A. uliginosa had a large distribution space and invasion potential in China, with the main distribution areas concentrated in southern, central and southwestern regions such as Guangxi, Guangdong, Fujian, Hainan and Taiwan. The low suitable area was mainly concentrated in Jiangxi, Hunan, Chongqing, Guizhou, Hunan and Zhejiang. By 2070, the overall suitable area of A. uliginosa in China is expected to decrease from 20.64% to 11.98%, with a decrease of 5.38% in low suitable areas and 3.28% in medium and high suitable areas. The area of low suitable areas in Sichuan, Yunnan, Guizhou, Hunan and Zhejiang has significantly reduced. The ROC curve analysis method showed that the average area under the curve (AUC) value is 0.968 with a standard deviation of 0.004, indicating that the prediction results are reliable. The knife-cutting method analysis showed that the four key environmental variables that have the greatest impact on the potential geographical distribution of A. uliginosa in China are Bio13 (precipitation of the wettest month), Bio16 (precipitation of the wettest season), Bio2 (mean diurnal temperature range), and Bio11 (mean temperature of the coldest season) are 27.6%, 22.5%, 11.1% and 8.0% respectively, indicating that precipitation and temperature range have a significant impact on the distribution of Viola palustris. The four key environmental variables that have the least impact on the potential geographical distribution of Viola palustris in China are Sq2 (soil nutrient retention capacity), Sq6 (soil toxicity), Bio9 (mean temperature of the driest season), and Sq5 (soil salinity). The four key environmental variables with the least impact on the potential geographical distribution of aquatic golden buttons in China are Sq6 toxicity, Sq7 operability, Bio1 annual average temperature, and Sq5 soil salt content. The potential suitable area for A. uliginosa is large in southern China and needs to be monitored to prevent further spread.