In order to facilitate lettuce fertilization in an economically rational way, an intelligent method to identify lettuce leaf nitrogen levels was studied. Lettuce samples of different nitrogen levels were cultivated in greenhouse with soilless cultivation method. In a particular growth period, the lettuce samples in various nitrogen levels were collected, then the FieldSpec○R3 spectrometer was used to acquire the hyperspectral data of the cultivated lettuce leaves. As there were much noise and redundant information in original hyperspectral data, standard normal variate transformation (SNV) was used to reduce the noise of the original hyperspectral data in this paper, then the principal component waves were extracted by principal component analysis (PCA). While K nearest neighbor (KNN) and support vector machine (SVM) were used for classification studies on the processed hyperspectra data respectively, adaptive boosting (Adaboost) was introduced into the two classifiers as it could improve the classification performance of weak classifiers, then Adaboost-KNN and Adaboost-SVM, the two integrated classification algorithms, were proposed. At last, the four classification algorithms were used for classification and identification of the same test sample data respectively, with the results showing that the classification accuracies of KNN, SVM, Adaboost-KNN and Adaboost-SVM were high up to 74.68%, 87.34%, 100% and 100%, among which the classification accuracies of Adaboost-KNN and Adaboost-SVM proposed in this paper were both good, and the stability of Adaboost-SVM was the best. Therefore, Adaboost-SVM used as a modeling method is suitable for the identification of lettuce leaf nitrogen level based on hyperspectrum, and it can also be used for reference to identify the nutrient elements of other crops in nondestructive testing methods.