Based on the three-step hybrid strategy of effective variable selection (rough selection, fine selection and optimal selection) in multivariate calibration of spectra, a feature variable selection method combining interval partial least squares (iPLS), interval variable iterative space shrinkage approach (iVISSA) and iteratively retaining informative variables (IRIV) was proposed to select the feature wavelengths of the near-infrared spectra of fresh chicken breast,and established a chicken moisture detection model. The number of modeled wavelengths was reduced by 0.76% after iPLS-iVISSA-IRIV, but the accuracy were stability of the model are gradually improved. The modeling results using the selected 8 characteristic wavelengths were as follows: correlation coefficient of calibration R_C=0.907 7, root mean square error of calibration RMSEC=0.516 1, correlation coefficient of prediction R_P=0.943 5, root mean square error of prediction RMSEP=0.612 3. The result shows that the iPLS-iVISSA-IRIV method based on the three-step hybrid strategy can effectively select the characteristic wavelengths of chicken moisture detection.