## 5 Discussion
For example, regarding \"Ease of Operation\" (PE1), user feedback indicated that the current interface presents a usability barrier for ICH practitioners and regular users, suggesting that a semi-enclosed, simplified interface could be developed to reduce complexity. In terms of \"Generation Speed\" (PE2), the large size of the FLUX.1-devmodel caused slower performance on standard PCs, and it was recommended to improve efficiency by introducing an accelerated generation package. The innovation of this study lies in the integration of traditional evaluation methods, AHP and IPA, with the development of the LoRA model, forming a new application pathway for artificial intelligence in the digital preservation of intangible cultural heritage. The AHP method provided an objective basis for the extraction of paper-cutting elements, ensuring the scientific and rational direction of model development. AHP clarified the cultural connotations of paper-cutting art and provided precise data support for subsequent LoRA model training. On this basis, the IPA method further validated the effectiveness of model development, closely linking quantitative research with model development. This combination not only provided a reliable content foundation for model generation but also offered clear directions for model optimization through user feedback. Limitations and Future Research Directions  
While the AHP method used in this study selected core paper-cutting cultural factors such as \"Spring Festival\" and \"Chinese Zodiac\" it was limited to the top four ranked factors. This limitation resulted in an incomplete representation of the full spectrum of paper-cutting culture, as other cultural elements (e.g., other animals, plants, and folk customs) were not adequately captured. Future research should expand the training dataset to include more paper-cutting cultural factors, thereby improving the effectiveness of the LoRA model in expressing the diversity of intangible cultural heritage and more comprehensively promoting and preserving paper-cutting art. Additionally, the AHP and IPA methods rely on questionnaire surveys, which introduce subjectivity due to the human intervention involved, potentially affecting the scientific validity and generalizability of the research results. Particularly, traditional artisans or ICH practitioners might have a biased understanding of artificial intelligence technology (e.g., algorithm aversion), leading to less objective evaluations of the model's actual performance. In the future, the number of survey respondents should be increased, and the scientific rigor of the questionnaire design should be improved to reduce potential bias, making the research results more representative. To address this issue, future research should incorporate more objective indicators and consider automated evaluation methods to reduce the impact of human subjectivity. Moreover, developing more user-friendly interfaces, simplifying operational processes, and lowering technical barriers could help further popularize the application of LoRA models in the cultural transmission field. Future research should further explore the role of AI in the creative processes of intangible cultural heritage practitioners and artisans, analyzing the potential impact of AI as an assistive or creative generation tool on their work. This will help deepen the understanding of AI's multifaceted role in cultural heritage preservation and innovative development. In terms of generation speed and hardware resource constraints, the large size of the FLUX.1-dev model made it difficult to run on standard PCs, resulting in slower generation speeds and negatively affecting the user experience. Although Stable Diffusion 1.5 performed better in terms of generation speed, there is still room for optimization when handling complex patterns.