Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (R_p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600–1000 nm spectra with R_p, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 ^oBx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.