## 4 Conclusions  
The generated landscape paintings of traditional CUT often have the problems of detail loss, blurred outlines, and poor style transfer. In this paper, we propose a generation model (Paint-CUT) to generate landscape paintings from input landscape photos. The key findings and contributions of Paint-CUT are introducing the SA-ResBlock, perceptual loss, and edge loss to address the above problems. Specifically, in order to solve the problem of detail loss, based on the layout and texture features of landscape paintings, the SA-ResBlock with shuffle attention is proposed to extract the features. In order to solve the problem of poor style transfer, based on the unique style of landscape paintings, the perceptual loss is introduced to constrain the model both in content and style, which guides the transfer from landscape photos to landscape paintings. In order to solve the problem of blurred outlines and generate landscape paintings with clear lines and edges, the edge loss is constructed to calculate the error between the edge map of the landscape photo and the edge map of the generated landscape painting. After the analysis of the training process, the qualitative and quantitative analysis of comparison and ablation experiments, the results prove that the landscape paintings generated by Paint-CUT are not only consistent with the content of the landscape photos but also consistent with the style of the target landscape paintings. The generated landscape paintings have clear lines and rich details, which improve the quality of landscape paintings. The generated landscape paintings based on the proposed Paint-CUT are consistent with the content of the photos and the style of the target landscape paintings. The intelligent generation of landscape paintings can effectively preserve the artistic value of landscape paintings and improve modern people's interest in traditional culture. Popularization of traditional Chinese culture can be promoted and public awareness of cultural protection can be enhanced. Consequently, the main purpose of this paper is to further integrate the culture and technology and provide a strong technological assistance for the inheritance and development of traditional Chinese culture. However, as one of the precious cultural heritages, Chinese landscape paintings mainly describe the characteristics of diverse landscape scenes, embody a unique style and mood, and use lines and special techniques to depict the outlines and details of scenes. The task of generating landscape paintings is more difficult than natural images due to the different scales of scenes and special painting techniques in handling different styles. The extraction ability of multi-scale information and generation quality of complex strokes still need to be improved, which are potential challenges of the proposed model. And the generated figures should present better results, such as clearer lines and a more rational layout. In the future, we will continue to address limitations and improve the generation quality of our model according to landscape painting characteristics. Firstly, we will focus on the special painting techniques of landscape paintings and learn how to better extract multi-scale features. Secondly, evaluation metrics specifically for landscape painting will be proposed to better evaluate the generated results. Finally, software or systems about Chinese landscape painting generation can be developed for exhibition and aesthetic education to promote the integration of culture and technology. Conceptualization, Z.S. ; methodology, Z.S. ; validation, H.L. and X.W. ; investigation, H.L. ; data curation, H.L. ; writing--original draft preparation, Z.S. and H.L. ; writing--review and editing, Z.S., H.L. and X.W. ; supervision, Z.S. ; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Program of China (No. 2017 YFB1402102), the National Natural Science Foundation of China (No.