The use of hyperspectral remote sensing technology to monitor the protein content related to grain quality before rice matures is important. It can promptly adjust cultivation management methods and guide reasonable fertilization and help to grasp rice grain quality information in advance and clarify market positioning. This study took typical high-quality Indica rice in Guangdong Province as the research goal. Two-year nitrogen gradient experiments were carried on in 2019 and 2020. The canopy level hyperspectral data and rice nitrogen parameters, including leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), plant nitrogen content (PNC), and plant nitrogen accumulation (PNA), were collected at the rice panicle initiation stage and heading stage. Then, four individual machine learning algorithms, Partial Least Square Regression (PLSR), K-Nearest Neighbor (KNN), Bayesian Ridge Regression (BRR), Support Vector Regression (SVR), and three ensemble learning algorithms, Random forest (RF), Adaboost, Bagging were used for monitoring and modeling the nitrogen status of rice at different growth stages. After that, the rice grain protein content estimation models based on rice canopy spectral information, and spectral information combined with rice nitrogen parameterswere constructed by different machine learning algorithms. The rice nitrogen and grain protein content estimation models' accuracy were evaluated and compared. The study results showed that for rice nitrogen nutrition monitoring, using the rice canopy spectral information from 454~950 nm, the R² of LNC, LNA, PNC and PNA estimation models based on RF and Adaboost algorithms achieved above 0.90 at the rice, heading stage, with low RMSE and MAE. Panicle initiation stage When using full-band spectral information to estimaterice grain protein content, RF had the highest accuracy and stability, with R² of 0.935 and 0.941, RMSE of 0.235 and 0.226, and MAE of 0.189 and 0.152 at rice panicle initiation and heading stage, respectively. Adaboost model has the highest accuracy and stability for seed protein monitoring with full-band spectral information combined with growth parameters at both fertility stages, at the panicle initiation stage, the Adaboost algorithm with full-band spectral and PNA data can reach the bestfor rice grain proteinestimation, the R², RMSE and MAE was 0.960, 0.175, and 0.150. While at heading stage, the R²,RMSE and MAE was 0.963, 0.170, 0.137,when using Adaboost algorithm with the full-band spectral and PNC data as input parameters. The results showed that the ensemble algorithms RF, Adaboost and Bagging have good ability to deal with multiple covariance compared with several individual learner algorithms PLSR, KNN, BRR and SVR.And they are suitable for the analysis and processing of hyperspectral data, which have obvious advantages in crop nitrogen nutrition monitoring and rice quality early monitoring through remote sensing.