Near-infrared spectroscopy has been widely used due to its high efficiency and non-destructive properties advantages. However, the consistency phenomenon between near-infrared spectrometers can lead to insufficient accuracy when the master model predicts the spectra of its slave instruments. If the calibration model is rebuilt based on the offset spectrum, it will lead to higher consumption. This paper proposes a transfer component analysis direct standardization (TCADS) algorithm to address the above issues.The algorithm initially employs an enhanced TCA algorithm to convert master and enslaved person spectra, which adhere to distinct distributions, by projecting them into high-dimensional reproducing kernel Hilbert space. Subsequently, it reduces the dimensionality of their spectral matrices. Finally, a direct standardization algorithm is reapplied to the master and slave spectra post-TCA transformation, further enhancing the model’s transfer performance. This algorithm combines nonlinear correction with linear correction, effectively alleviating the problem of overcorrection compared to traditional linear correction algorithms, and is robust. To verify the effectiveness of the algorithm, experiments were conducted on public datasets and compared with traditional direct standardization (DS), piecewise direct standardization (PDS), and slope and bias correction (SBC) methods. The experiment demonstrates that the TCADS algorithm proposed in this article efficiently minimizes spectral disparities between the master instrument and the slave instrument. This enhancement notably outperforms traditional model transfer algorithms, facilitating the effective sharing of near-infrared spectral models established on the master instrument to the slave instrument.