The work aims to apply near infrared nondestructive testing technology to the intelligent packaging production line to detect the freshness of blueberries packaged with PE film accurately and rapidly. The moisture content and soluble solids content (SSC) of blueberries were taken as evaluation indexes. Furthermore methods of SNV, MSC and DT combined with UVE were used to obtain spectral data. Then PLSR and RF were used to build prediction models of moisture content and SSC of bulk and PE packaged blueberries. The accuracy of model was verified by comparing the determination coefficient and root mean square error. The prediction model of moisture content of bulk blueberries was established. SNV and PLSR were the best pre-processing and modeling methods, respectively. The best PCA was 9, Rc2 was 0.971, Rp2 was 0.933; For establishing a prediction model of moisture content of blueberries packaged with PE film, SNV and RF were the best pre-processing and modeling methods, respectively. Rc2 was 0.923, Rp2 was 0.876; For establishing a prediction model of SSC of bulk blueberry, DT combined with UVE, and RF were the best pre-processing and modeling methods, respectively. Rc2 was 0.942, Rp2 was 0.869; For establishing a prediction model of SSC of blueberries packaged with PE film, MSC combined with UVE, and PLSR were the best pre-processing and modeling methods, respectively. The best PCA was 7, Rc2 was 0.849, Rp2 was 0.707. By comparing the prediction models of bulk blueberries and PE packaged blueberries, it is found that PE film affects the accuracy of the prediction model, but doesn't affect the use of it. The work provides a practical method for rapid nondestructive testing of blueberry freshness in intelligent packaging production line.