With the rapid development of the animation industry， the face generation with animation characters becomes a key technology. The existing style transfer technology of painting style cannot obtain satisfactory animation results due to the following characteristics of animation： （1） Animation has a highly simplified and abstract unique style， and （2） animation tends to have clear edges and smooth shadows and relatively simple textures， which poses great challenges to the loss function in existing methods. This paper proposes a novel loss function suitable for animation. In the loss function， the semantic loss is expressed as a regularized form in the high-level feature map of the VGG network to deal with the different styles between real and animation images， and the edge sharpness loss with edge enhancement can preserve the edge sharpness of animation images. Experiments on the four public data sets show that through the proposed loss function， clear and vivid animation images can be generated. Moreover， in the CK+ data set， the recognition rate of the proposed method is increased by 0.43% （Miyazaki Hayao style） and 3.29% （Makoto Shinkai style） compared with the existing method， increased by 0.85% （Miyazaki Hayao style） and 2.42% （Makoto Shinkai style） in the RAF data set， and increased by 0.71% （Miyazaki Hayao style） and 3.14% （Makoto Shinkai style） in the SFEW data set， respectively. The generation effect in the Celeba data set is also demonstrated. The above results show that the proposed method combines the advantages of the deep learning model to make the detection result more accurate.