As more and more spam emails are continually increasing exponentially, both the Internet Service Providers (ISP) and the end users are suffering. Spam filtering is a classic puzzle in the field of network security. In this paper, we present a novel anti-spam technique standing at a unique point of view, which includes novel users? feedback mechanism, useroriented classifier based on improved Na?ve Bayesian approach. We also implement a prototype and evaluate the technique by using both well-known mail corpus and real dataset collected from the mail server of our institute. The results demonstrate that the novel technique has relatively lower false positives, better performances than traditional techniques and it is a good enterprise solution for spam filtering.