Today, people use email services such as Gmail, Outlook, AOL Mail, etc. to communicate with each other as quickly as possible to send information and official letters. Spam or junk mail is a major challenge to this type of communication, usually sent by botnets with the aim of advertising, harming and stealing information in bulk to different people. Receiving unwanted spam emails on a daily basis fills up the inbox folder. Therefore, spam detection is a fundamental challenge, so far many works have been done to detect spam using clustering and text categorisation methods. In this article, the author has used the spaCy natural language processing library and 3 machine learning (ML) algorithms Naive Bayes (NB), Decision Tree C45 and Multilayer Perceptron (MLP) in the Python programming language to detect spam emails collected from the Gmail service. Observations show the accuracy rate (96%) of the Multilayer Perceptron (MLP) algorithm in spam detection.

翻译：如今，人们使用Gmail、Outlook、AOL Mail等电子邮件服务快速发送信息和官方文件进行交流。垃圾邮件或垃圾邮件是这类通信面临的主要挑战，通常由僵尸网络批量发送，旨在向不同人群进行广告宣传、造成损害和窃取信息。每天收到不想要的垃圾邮件会填满收件箱文件夹。因此，垃圾邮件检测是一项基本挑战，迄今为止已有许多工作使用聚类和文本分类方法进行垃圾邮件检测。在本文中，作者使用spaCy自然语言处理库以及Python编程语言中的三种机器学习算法——朴素贝叶斯、决策树C45和多层感知器（MLP），对从Gmail服务收集的电子邮件进行垃圾邮件检测。观察结果表明，多层感知器（MLP）算法在垃圾邮件检测中的准确率达到96%。