Near-infrared spectroscopy (NIRS) technology has distinct advantages in component detection for its characteristics of high-speed and low-cost, which is essential for the supervision of drug quality and safety. Studying the method of drug component detection based on NIRS technology is significant for improving the level of drug quality supervision. In fact, owing to differences in performance parameters of different spectroscopic instruments, spectra measured are discrepancy, which brings hardship to the realization for quantitative correction models sharing. Therefore, in order to improve analysis efficiency, the calibration transfer method is discussed. In this paper, the establishment of cephalosporins component correction model and calibration transfer method are studied, and a transformation set selection method based on Markov chain (MC) is proposed. Fifty-six samples of cefradine granules in different batches were used. Spectral data were measured by two Fourier spectrometers. For three components of the sample: cefradine, Cefalexin and water, partial least squares (PLS) method was used to establish a quantitative correction model. MC algorithm is used to construct the probability matrix and select the conversion set, which improves the efficiency of model transformation and the prediction accuracy of spectral data. The experimental results show that the quantitative calibration model transfer between different spectroscopic instruments can be realized by using a small number of sample sets. After the model transfer, the relative error of the quantitative calibration model for the three principal components prediction decreases from 9.67%, 52.14%, 19.25% to 4.37%, 31.12%, 11.67%, respectively. The spectral differences between master and slave instruments can be corrected effectively, and the transfer and sharing of measurement spectra and quantitative analysis models of different instruments can be realized. The modeling analysis and model transfer methods studied in this paper also provide technical support for drug composition and quality detection.