Using hyperspectrum technology to measure the soluble solid content (SSC) of pitaya can provide a scientific method for the non- destructive measurement of interior quality of pitaya.Pretreatment methods are used to process diffuse reflectance spectroscopy.The successive projections algorithm (SPA) is used to select characteristic variables.The partial least squares model (PLS) and back propagation neural network model (BPNN) are built to predict the SSC of pitaya.The influence of the pitaya peel on the SSC model prediction accuracy is also analyzed.The result of moving average smoothing (MAS) pretreatment method is best.The correlation coefficient of cross calibration (Rcv) of the PLS model built is 0.8635,root mean square error of cross validation (RMSECV) reaches 0.6791.Based on the 15 characteristic variables,the correlation coefficient of prediction (RP) of the BPNN model for predicting SSC of pitaya is 0.8411,and the root mean square error of prediction (RMSEP) is 0.8171.The peel has influence on prediction results of models,RP of the whole pitaya model is 0.8999,and RMSEP is 0.7208;RP of the pitaya model without peel is 0.9304,and RMSEP is 0.5291.The prediction accuracy of the pitaya model without peel is higher than the whole pitaya model.The results indicate that the non-destructive measurement for SSC of pitaya based on hyperspectrum technology is feasible.