In order to explore the feasibility of prediction soluble solid contents (SSC) in sugarcane stalks by using near infrared hyperspectral imaging techniques,two hundred and forty sugarcane stalks which come from three different varieties were studied.After obtaining the raw hyperspectral images of sugarcane stalks,the spectral information and textural features were discussed respectively.The prediction models were established by using partial least squares regression (PLSR),principal components regression (PCR) and least squares support vector machines (LS-SVM) algorithms.Besides,three different selected wavelengths algorithms such as successive projection (SPA) algorithms,intervals partial least squares (iPLS) algorithms and uninformation variables elimination (UVE) algorithm were analyzed after building partial least squares regression model.The results indicate that partial least squares regression model based on spectral features can be an steady model to predict SSC and the correlation coefficient (R2)　of calibration sets and prediction sets are 0.879,0.843.The root mean square errors of calibration sets and prediction sets are 0.644,0.742 respectively.The obtained 105 wavelengths which were selected by UVE algorithm are effective spectral features.The R2 results of calibration sets and prediction sets of its PLSR model are 0.860,0.813.The root mean square errors of calibration sets and prediction sets are 0.693,0.810 respectively.