Through observing and analyzing these celestial spectra, astronomers can obtain effective astronomical information to research the theory of astronomy and astrophysics. And a large scientific engineering project in China, the LAMOST survey program can obtain a large number of celestial spectra every observation night. For the massive data, it is very important to find an automatic method to analyze the celestial spectrum and measure various physical parameters of the celestial body. In fact, many scholars had been attracted to research this topic before, however, the current algorithms and corresponding results that were presented by them cannot meet the accuracy of manual measurement, and it means that we should find more appropriate algorithms to improve the effect of automatic processing. In this paper, we study deeply the applications of the Kernel Ridge Regression (hereinafter referred to as KRR) method in the automatic measurement of the stellar atmosphere physical parameters (including temperature, gravity and chemical abundance), especially the applications in the spectral data released by LAMOST. The KRR is a further development of the Ridge Regression algorithm, and the Ridge Regression is a variant of the Least Squares Method with a regularization term, it has an ability to solve high dimensional multi-collinearity problems. Therefore, the KRR method is suitable for processing high-dimensional celestial spectral information. In this paper, 20 000 stellar spectral data identified as stars are randomly selected from the release data of LAMOST for experimental testing. The data set contain spectra with low SNR and high SNR (the average SNR in g, r, i-band are from 6.7 up to 793). In this paper, we preprocess all the spectra firstly, including three steps: one is the de-noise phase based on the wavelet transform; In order to avoid some inaccuracies in the spectral flux value, the second step is spectral flux normalization; Because the dimension of each spectrum is up to several thousand dimensions, the principal component analysis method (PCA) is used to reduce the spectral dimensions as the third step. Then, we establish a regression model between the spectral data and the normalized three stellar parameters based on the KRR method. Finally, we design many different combinations of experiments to test and analyze the KRR model, and compare its results with the results of the classical algorithm SVR. All the experimental results using KRR method show that the average absolute error of temperature, gravity and chemical abundance is 82.989 7 K, 0.185 8 dex and 0.121 1 dex, respectively, it is better than the result of the SVR which is 144.230 8 K, 0.188 6 dex and 0.124 6 dex, respectively. In particular, the KRR method has a large advantage in temperature test results, which indicates that the KRR method can be effectively applied to the automatic measurement of the stellar spectral parameters.