For the rapid detection of SPAD value of cucumber leaves, the calibration model of SPAD value was built by using visible and near infrared (Vis/NIR) spectroscopy technique and chemometrics methods. Different calibration methods were used to build the model in the whole wavelength region. The results indicate that the optimal performance is achieved by least squares support vector machine(LSSVM) model, and the correlation coefficient ( r ) and root mean squares error of prediction (RMSEP) are 0.9583 and 0.9732, respectively. Via the analysis of correlation coefficients between the spectral reflectance and SPAD values, and the regression coefficients of partial least squares (PLS), two characteristic wavelength bands (531～581nm and 696～716nm) and four characteristic wavelengths (556, 581, 698 and 715nm) were obtained. LSSVM was used to the aforementioned wavelength bands and wavelengths. The results indicate that the characteristic wavelength bands can achieve a better performance with r of 0.9338 and RMSEP of 1.1370. The prediction results are similar to the whole wavelength region model, while, the performance of LSSVM model with four characteristic wavelengths was not satisfying. Calibration method of using characteristic wavelength bands can largely reduce the calibration computation, and increase the calibration speed. Hence, it is more effective to use characteristic wavelength bands for the detection of SPAD values of cucumber leaves.