Near-infrared spectroscopy analysis technology has the advantages of low cost, high efficiency, and pollution-free. In recent years, it has been widely used in qualitative and quantitative analysis in various fields. Multivariate calibration technology is the most advanced technology in the field of spectroscopy. Changes in conditions, instruments, or substances may cause the multivariate calibration model to no longer be suitable for the prediction purposes of newly measured samples. Re-calibration and re-modeling will inevitably waste a lot of time and resources; another option is calibration transfer, which extends the existing calibration model in the source domain to the target domain to avoid the cost of repeated modeling. In the related chemometrics literature, most transfer methods need to measure a set of transfer standard samples under the same conditions of two instruments. However, in the near-infrared spectroscopy measurement technology, due to the characteristics of volatilization of the standard samples, It is not easy to obtain and save the standard samples for constructing the transfer method for instrument calibration. This paper proposes a joint feature subspace distribution alignment (JSDA) calibration transfer method in response to these problems. This method can establish a calibration transfer model without a standard sample from the instrument. JSDA first establishes the joint PCA subspace (Principal component analysis) of the data features of the source and target domains; then corrects the calibration model by aligning the source domain feature distribution and target domain feature distribution mapped in the joint feature subspace; Finally, the least squares model is used to build a calibration model on the corrected source domain, which can be directly used for the calibration of the target domain. The experimental results show that compared with the existing mature calibration transfer methods, JSDA has more advantages in predicting performance on public real data sets, which verifies the effectiveness and superiority of the model in practical applications.