Multi-domain facial expression transfer entails the mutual transformation between different images to generate high-fidelity facial images with source facial expressions and target facial identity features, solve the problem of high similarity and low image authenticity of images generated by traditional methods. This study proposes a multi-domain facial expression transfer model based on the improved StarGAN-V2. The model consists of a generator, a discriminator, a mapping network, and a style encoder. The spatial attention mechanism is introduced, and the cycle consistency loss is upgraded to an adversarial cycle consistency loss. A new domain feedback discriminator is appended after the generator. The improved StarGAN-V2 model can generate high-fidelity facial images with source facial expressions and target facial identity features based on the source and target images. Experimental results show that for the improved model, the FID values of latent guided synthesis and reference guided synthesis are 11.9 and 17.4 respectively, and the LPIPS values are 0.491 and 0.426 respectively. These values are better than those of the control model. The improved model solves the problem of high image similarity and generates more realistic images.