To be able to quickly identify the cucumber real time, the present paper studied the near infrared reflectance characteristics of cucumber, stem and leaf. Spectral reflectance of 138 samples (46 cucumbers, 46 stems and 46 leaves) was collected using near infrared spectroscopy in the band range of 600～1 099 nm indoor. After Savitzky-Golay smoothing preprocessing, random 108 spectral samples were put forward as calibration set. The weighted deviation method was used for choosing the spectral bands 690～950 nm that include much more information. The samples were analyzed by PCA method to extract the principal component scores, combining the Mahalanobis distance method the recognition model was established, and seven abnormal samples were excluded. The partial least squares (PLS) model was established by remaining 101 samples spectra of calibration set, which was used for predicting the validation set (30 samples except of the calibration set). The result shows that the correlation of the predicted value and the actual value reaches up to 0.994 1, and the correct recognition rate is 100%. This significantly illustrates that the near infrared spectral reflectance characteristics are different among the cucumbers, stems and leaves, which can be successfully applied to recognition of cucumber by the method. The developed technique can provide a new method for cucumber identification.