Abstract: ObjectiveSupport vector machine optimized by parameters was applied to predict classification of nitro-gen application level for rice in order to provide scientific basis for accurate fertilization and high yield management of rice.MethodFour nitrogen application levels(from high to low,the amount of pure nitrogen was 225,150,75 and 0 kg/ha respectively)were set,and rice cultivar Jinyou 458 was used as experiment material. The SPAD values of the 6th to 9th phyllotaxis rice leaves were obtained by chlorophyll meter SPAD-502(SPAD value of leaf top,leaf middle and leaf bot-tom). The SPAD values of rice leaves under four nitrogen application levels were trained and predicted by using support vector machine optimized by particle swarm optimization and grid search algorithm.ResultFor the 7th and 8th phyllotaxis leaf combination,the 7th,8th and 9th phyllotaxis leaf combination and the 6th,7th and 8th phyllotaxis leaf combination,the rice nitrogen application rate classification detected by support vector machine optimized by particle swarm optimization was better than support vector machine optimized by grid search algorithm,its accuracy was 75.000%higher. Moreover, its accuracy on the 7th and 8th phyllotaxis normalized leaf combination was the highest(88.889%).ConclusionSupport vector machine optimized by particle swarm optimization is suitable for predict the classification of rice nitrogen applica-tion levels and meets the needs of agricultural research.