基于深度学习的垃圾邮件文本分类方法

文中旨在研究基于深度学习的垃圾邮件文本分类方法，该方法结合了卷积神经网络（CNN）和循环神经网络（RNN）的模型，通过对邮件文本进行特征提取和分类，能高效、准确地对垃圾邮件进行分类。文中以卷积神经网络和循环神经网络为实验对象，提出了一种垃圾邮件文本分类方法，并在公开数据集上进行了实验。实验结果表明，该方法在垃圾邮件文本分类任务上具有较高的准确率和召回率。

Spam Text Classification Method Based on Deep Learning

This paper aims to study a deep learning-based email spam text classification method. This method combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) models to efficiently and accurately classify spam by extracting and classifying features from email texts. In this paper, convolutional neural networks and recurrent neural networks are used as experimental objects, and an email spam text classification method is proposed, and experiments are carried out on public datasets. Experimental results show that the method has high accuracy and recall rate on email spam text classification tasks