This paper focuses on a novel Electroencephalography (EEG) based one dimensional convolution neural network (CNN) to classify emotional states. Differential entropy (DE) is considered as a feature extraction method after pre-processing phase. Besides, feature smoothing-linear dynamic system (LDS) and min-max normalization are used on the DE features before feeding into deep model. We design a one dimensional CNN model with six convolutions and fully connected blocks which gives outstanding performance in six combinations of SEED dataset. The model presented average accuracy of 98.55% and 95.91% in binary and single sessions respectively by using 10 fold cross validation. The proposed results fully demonstrate that our method achieves out of the best performance compare with other EEG based emotion recognition systems. Therefore, this model can be applied to other emotional datasets as a classifier and health care decision support system (DSS) as well.