In order to identify Lonicerae Japonicae Flos and Lonicerae Flos rapidly and precisely, a hyperspectral imaging technology combined with chemometric methods was applied to develop the nondestructive identification models for Lonicerae Japonicae Flos and Lonicerae Flos. Firstly, the original spectral data were analyzed by three pretreatment methods including Savitzky-Golay (SG) convolution smoothing, Multiple Scatter Correct (MSC) and Standard Normal Variate Transformation (SNV). A comparison was made among SG, MSC and SNV based on Partial Least Squares (PLS), of which the best pretreatment method was SNV. The Regression Coefficient (RC) and Successive Projection Algorithm (SPA) were used to extract the characteristic wavelengths after SNV pretreatment. Extreme learning machine (ELM) and Last Squares Support Vector Machine (LS-SVM) were applied to build the classification models based on characteristic wavelengths. This results revealed that the LS-SVM model based on SPA performed the optimal classification, with the accuracy of all 100% for modeling set and prediction set. Therefore, hyperspectral imaging technology can be used to identify Lonicerae Japonicae Flos and Lonicerae Flos effectively and non-destructively based on full wavelengths and characteristic wavelengths.