Diversified Generation and Evaluation of English Cultural Communication Materials Based on Generative Adversarial Networks (GANs)

English has become the most widely used language in the world. In the fields of English education and cultural dissemination, traditional teaching methods and materials are no longer able to meet the growing demand for cultural understanding and communication. This study constructed a Generative Adversarial Networks (GANs) model, which generates highly realistic and diverse cultural communication materials by training generators and discriminators. The generator generates data from random noise, while the discriminator distinguishes between generated data and real data. The experimental results show that the Fréchet Inception Distance (FID) value of the material generated by GANs is much lower than that of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), exhibiting higher image realism; its Mode Coverage (MC) value is also higher than other methods, averaging 90.428%. In addition, the audience showed high click through rates and engagement with the materials generated by GANs, verifying their effectiveness in cultural communication and confirming the potential of GANs in generating diverse English cultural communication materials.