A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored. The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable filter (AOTF) NIR spectrometer. To make full use of the effective information of NIR spectral data, discrete binary particle swarm optimization (DBPSO) algorithm was used to select the optimal wavelength variates. The new spectral data, composed of absorbance at selected wavelengths, were used to create the thickness quantitative analysis model by kernel partial least squares (KPLS) algorithm coupled with Boosting. The results of contrast experiments showed that the Boosting-KPLS model could efficiently improve the analysis accuracy and speed. It indicates that Boosting-KPLS is a more accurate and robust analysis method than KPLS for NIR spectral analysis. The maximal and minimal absolute error of 30 testing samples is respectively -0.02 μm and 0.19 μm, and the maximal relative error is 14.23%. These analysis results completely meet the practical measurement need.