Seed purity reflects the degree of seed varieties in typical consistent characteristics,so it is great important to improve the reliability and accuracy of seed purity detection to guarantee the quality of seeds.Hyperspectral imaging can reflect the internal and external characteristics of seeds at the same time,which has been widely used in nondestructive detection of agricultural products.The essence of nondestructive detection of agricultural products using hyperspectral imaging technique is to establish the mathematical model between the spectral information and the quality of agricultural products.Since the spectral information is easily affected by the sample growth environment,the stability and generalization of model would weaken when the test samples harvested from different origin and year.Active learning algorithm was investigated to add representative samples to expand the sample space for the original model,so as to implement the rapid update of the model’s ability.Random selection (RS) and Kennard- Stone algorithm (KS) were performed to compare the model update effect with active learning algorithm.The experimental results indicated that in the division of different proportion of sample set (1∶1,3∶1,4∶1),the updated purity detection model for maize seeds from 2010 year which was added 40 samples selected by active learning algorithm from 2011 year increased the prediction accuracy for 2011 new samples from 47%,33.75%,49% to 98.89%,98.33%,98.33%.For the updated purity detection model of 2011 year,its prediction accuracy for 2010 new samples increased by 50.83%,54.58%,53.75% to 94.57%,94.02%,94.57% after adding 56 new samples from 2010 year.Meanwhile the effect of model updated by active learning algorithm was better than that of RS and KS.Therefore,the update for purity detection model of maize seeds is feasible by active learning algorithm.