Wood information intelligent acquiring is the key for timber quality tracing. It is also the prerequisite for timber sorting， processing and fine applications. This study aims to discuss the denoising of aspen wood near infrared spectroscopy (NIRS) with wavelet transform and develop the calibration model for wood density， to analyze the feasibility of NIR-based wood quality tracing. In this study， calibration model was developed for air-dry density of aspen wood based on NIRS and partial least squares (PLS) algorithm. Wavelet transform was used for NIR denoising treatment and model optimization. The best denoising method was determined. Aspen wood density predicted with NIR calibration model together with other wood information (species， locality of growth， measuring unit， ways of data acquisition etc. ) was recorded with QR (Quick Response) code for the quick and effective tracing. The readability and effectiveness of the QR code with varied correction levels， number of characters， and pixel sizes were compared. The results showed that： (1) The best model fitting was achieved with the decomposition layer of 5 (db5 wavelet) under the heuristic hard threshold denoising treatment. The determination coefficient (R2) was increased from 0774 8 to 0850 1 for the PLS calibration model. (2) As the number of coded characters were 217 in this study， the readability of QR code was low with pixel size of 100 px×100 px while the QR code readability was higher than 90% with pixel size greater than 100 px×100 px. The readability could be 100% with pixel size of 200 px×200 px and number of coded characters up to 600 at error correction level of 7%. It can be concluded that QR code could be an effective carrier for timber tracing information acquired with NIRS.