Tofu was a traditional food in China in two thousand years. However, it is still mainly produced by individual workshop, and the security and uniformity of quality is difficult to guarantee. Water and protein content are important factors which affecting the quality of tofu. The traditional detection method for water and protein is complicated, time consuming and laborious. The detection results are often later than the production process which unable to guide tofu production in time. Therefore, it is necessary to develop a new method for quantitatively describing homogeneity of water and protein distribution in Tofu. It is also the a scientific basis for accurately regulating the production process of tofu. In this study, hyperspectral imaging technique combined with chemometrics method were used to detect the changes of water and protein content and distribution under four different conditions: soybean milk, hot soybean milk, gel and tofu. The hyperspectral image of 120 samples in each state was collected in the wavelength range of 432 to 963 nm. Use ENVI software to select the region of interest and calculate the average spectral data of the sample. The original spectrum was pre-processed by convolution smoothing (Savitzky-Golay, SG) as well as multiplicative scatter correction (MSC) to eliminate the influence of spectral noise. A partial least squares regression (PLSR) and principal component regression (PCR) quantitative model were established for the pre-processed spectral data. The prediction results of water and protein by using PCR model are lower than the PLSR model, therefore, the PLSR model is selected as the optimal model. The characteristic wavelengths of soybean milk, hot soybean milk, gel and tofu samples were selected by continuous projection algorithm (SPA), 13, 90, 8 and 9 characteristic wavelengths to establish the PLSR model based on the characteristic wavelengths. The results show that the SPA+PLSR model based on the characteristic wavelengths is better than the PLSR model under the full-band model. The prediction model R_p for for water is 0.84~0.96, and the prediction model R_p for protein is 0.92~0.97. Based on the SPA+PLSR model with better prediction effect, the water and protein contents of different states pixel in the image of soybean milk, hot soybean milk, gel and tofu, were calculated and the water and protein distributions in the sample were visualized with different colors. The feasibility of hyperspectral technology for detecting water and protein contents in tofu was verified, which could be used to solve the defects of traditional detection methods, as well as provided a theoretical basis for the industrialization and intelligence of tofu production.