Spam filtering is an important issue in the information age. And, if an important email is wrongly classified, it would lead to an immeasurable cost. Thus, in the field of spam filtering, the ways to improve the accuracy and recall of the filters is the key issue. At present, the binary classification model in machine learning is usually used to deal with spam filtering. However, compared with the three-way decisions, the binary classification model usually leads to a higher cost of misclassification. And, as an important branch of three-way decisions, the three-way decisions with decision-theoretic rough sets can effectively reduce the misclassification cost and further improve the performance of filters. And, it also conforms to human cognition. Nevertheless, few studies consider the effect on classification results induced by the differences among equivalence classes when constructing the loss functions. Therefore, under the framework of the three-way decisions with decision-theoretic rough sets, an adaptive three-way spam filter with similarity measure is proposed. The model calculates the weights of condition attributes according to set variance firstly. Then, a comprehensive evaluation function for describing difference information among equivalence classes based on similarity measure of set is established. Finally, an adaptive model for calculating threshold pairs based on Bayesian decision rules is constructed. Experimental results show that the proposed model performs well in the field of spam filtering.