To tackle the problem that the existing face image translation models cannot realize the translation among multiple visual attributes and the translated face images are not clear and natural, this study proposes a multi-attribute face image translation model based on the face recognition method. The model is mainly composed of the content and style encoder, AdaIN decoder, and face recognition module. First, the two encoders extract the potential encoding of the content and style image and then send the encoding into the AdaIN layer for affine transformation, and finally the decoder restores the translated image. A face recognition model is designed and trained using this method with an accuracy rate of 90.282%. A joint face attribute loss function is proposed, which enhances the model’s attention to the attributes of the style face, solves the problem that the model cannot accurately extract the attribute information of the face, and discards irrelevant information so that the model can generate clear, multi-attribute, and diverse face translation images. This method is tested on the open dataset CelebA-HQ, whose results are higher than the baselines in terms of both quantitative and qualitative indicators. It also shows good robustness in different face orientations. The image generated by the model can also be used in the field of face image generation to address dataset shortage.