Abstract: E-mail is an important communication tool, but the problem of spam has been affecting peoples daily work and life. Continuously improving spam detection technology and increasing the speed and accuracy of spam detection has important research and practical significance. Bi-directional gated recurrent unit (BiGRU) and convolutional neural network (CNN) are widely used in the field of text classification. The combination of them could give full play to BiGRU context dependency extraction capabilities and CNN feature extraction capabilities. But for the problem of spam recognition, it is also necessary to consider some specific words in the email. So this article proposes a spam recognition method based on the BiGRU-Attention-CNN model to improve the accuracy of spam detection. The model first converts the email text into feature vectors and performs BiGRU serialization learning, and then introduces the attention mechanism (Attention) to give greater weight to specific words. After the attention layer is input to the CNN model, through convolution, pooling, and full connection, the classification result is finally obtained. The model is tested on the Trec06c mail data set and compared with other models, better results are achieved. The final accuracy of the model is 91.62%.

Key words: BiGRU, attention, CNN, spam recognition