E-mail is one of the most widely recognised channels of communication, whether for personal or corporate purposes. The worst part about spam emails is that they intrude on user’s privacy without their consent, and the constant bombardment of spam mails fills up the user's entire email space. Additionally, the issue of wasting network capacity and time checking and deleting spam mails makes it an even more concerning issue. Although confrontation tactics are constantly being upgraded, the results of those methods are now unsatisfactory. Furthermore, phishing emails have been on the rise in recent years. To combat the issue of phishing emails, more effective phishing detection technology is required. We intend to create a phishing email detection tool that analyses the email structure first, using an upgraded Convolutional Neural Networks model with multilayer vectors and Long Short-Term Neural Network (LSTM) to model emails at the email header, character level, email content, and word level all at the same time. To assess the efficacy, we'll use an unbalanced dataset with actual phishing and genuine email ratios. The total accuracy of the experiment achieves a high percentage. 