Concerning the problem that Stack Auto-encoder (SA) easily traps to overfitting, which may reduce the accuracy of spam classification, a modified SA method based on dynamic dropout was proposed. Firstly, the specificity of the spam classification was analyzed, and the dropout algorithm was employed in SA to handle overfitting. Then according to the fault of dropout algorithm that making some nodes be in the stall state for a long time, an improved algorithm of dropout was proposed. The static dropout rate was replaced by dynamic dropout rate which decreased with training steps using dynamic function. Finally, the dynamic dropout algorithm was used to improve the pretraining model of SA. The simulation results show that compared with Support Vector Machine (SVM) and Back Propagation (BP) neural network, the average accuracy of the modified SA is 97.66%. And the Matthews correlation coefficient of every dataset is higher than 89%. Matthews correlation coefficient of the modified SA on every dataset is 3.27%, 1.68%, 2.16%, 1.51%, 1.58% and 1.07% higher than that of the conventional SA separately. The experimental results show that the modified SA using dynamic dropout has higher accuracy and better robustness.