In order to improve the spam identification speed without sacrificing the accuracy seriously

a novel quick online spam identication method is proposed.Firstly

the conceptions of user positive interest set and user negative interest set are introduced

and emails are classified by combining user interest sets and support vector machine.Secondly

based on the active learning theory

the sample densities of different categories and the improved angle diversity method are used to select the most uncertainly classified samples

and the selected samples are recommended to users for labeling.Finally

the labeled and the classified samples with greatest possiblities are put into the training set

and a novel sample value evaluating function is proposed to filter the redundant samples for generating a new training set.Experimental results show that

the sample labeling burden of the proposed method is small

the spam identification accuracy is high

and the spam identification speed is fast

the high value of the proposed method on online application is proved.