In the present paper, the kernel partial least squares regression (KPLSR) method was used to measure the atmospheric physical parameters (effective temperature, surface gravity, an d chemical abundance) based on the use of Lick line index. The proposed method can reduce the computation cost and achieve an ideal measure precision. At first, the Lick indices of Kurucz synthetic spectra were extracted and the kernel regression model between the Lick indices and the atmospheric physical parameters was established using the KPLSR method. Then the physical parameters of DR8 measured spectral data were computed by the kernel regression model for testing. The test results were compared with the atmospheric physical parameters provided by SEGUE SSPP and were good results. In addition, we added a signal-to-noise ratio (SNR) of 10, 20, 30, 40, 50, 70, 90 and 120 Gaussian white noise to the Kurucz spectra. And the resulting spectra of different SNR were used to test the impact of noise on the parameter measurement. The experimental results show that the kernel regression model is sensitive to noise, the higher the SNR of spectral data, the higher the prediction accuracy of the physical parameters. The method of KPLSR based on Lick line index has small amount of computation and fast training speed, which is suitable for measuring physical parameters of stellar atmosphere.