The key to the successful invasion of Spartina alterniflora is its ability to grow and reproduce and its ability to adapt to the environment. Leaf water content, relative chlorophyll content, carbon-to-nitrogen ratio, total nitrogen, total phosphorus, and specific leaf area, and other leaf functional traits reflect S. alterniflora's ability to utilize resources and adapt to the environment. This study was carried out in the coastal wetland of Yancheng, Jiangsu Province, to study the relationship between leaf functional characteristics of S. alterniflora and hyperspectral data. Principal component analysis was performed on the original spectral data and first-order differentially transformed spectral data to extract new principal component variables as new independent variables. Then stepwise regression, BP neural network, support vector machines, and random forest regression models for different leaf functional traits were established. The optimal model is selected by comparing the decision coefficient R² and Root mean square error (RMSE) of the constructed model, and then the optimal model is constructed based on the sensitivity band obtained by the correlation analysis to verify its accuracy and applicability. The results indicated that (1) the accuracy of the first derivative transformation of the hyperspectral data was better than that of the original spectral data. (2) Through predictive modeling of different leaf functional traits, it was found that the prediction effects of the four models are ranked as follows: random forest > support vector machines > BP neural network > stepwise regression. The random forest model had high accuracy and stability, which was obviously better than the other three models. The stepwise regression model had the worst effect and was not suitable for modeling and predicting leaf functional characteristics of S. alterniflora based on hyperspectral data. (3) Using the sensitive bands obtained by correlation analysis as independent variables, we established random forest models with different leaf functional traits. The R² of the constructed models were all greater than 0.90, and the R² of the verification model was between 0.73 and 0.95, which further confirmed the accuracy and stability of the random forest model. These results showed that the hyperspectral data can be used as a powerful means to quickly monitor the growth status of S. alterniflora, and the random forest model can be used as a high-precision model to estimate the different leaf functional characteristics of S. alterniflora.