## 5 Discussion  
(1) Advantages of the Paper-Cutting LoRA Model Development  
The LoRA model demonstrated significant advantages in generating complex cultural symbols, especially Chinese characters like the \"Fu (iii)\" character. Through LoRA fine-tuning, the flux_dev_fp8_Comfy and anything-v5-PrtRE.safetensors models excelled in detail capture and generation accuracy when handling complex symbols. By introducing low-rank matrices for fine-tuning, LoRA significantly enhances model performance without the need for large-scale retraining. This optimization is particularly effective with limited training data, allowing the model to better reproduce complex cultural elements in Chinese paper-cutting, which is crucial for the transmission of this art form. Specifically, after introducing LoRA, the flux_dev_fp8_Comfy model significantly improved the accuracy of generating complex symbols in paper-cutting, especially when processing Chinese characters with historical and cultural significance (such as the \"Fu (iii)\" character). Without LoRA, the base model exhibited considerable limitations in handling details, often producing distorted or incomplete symbols. For the anything-v5-PrtRE.safetensors model, LoRA notably improved the generation of intricate details in paper-cutting, making the patterns more aligned with traditional paper-cutting aesthetics and cultural connotations. In summary, the LoRA model showed clear advantages across both base models. While the flux_dev_fp8_Comfy model performed exceptionally well in handling complex tasks such as Chinese character generation, its speed did not improve significantly when dealing with simpler patterns. In contrast, anything-v5-PrtRE.safetensors performed more balanced under resource-constrained conditions, making it suitable for large-scale generation tasks. Therefore, the use of LoRA should be chosen based on task complexity and resource requirements. flux_dev_fp8_Comfy is better suited for high-precision and complex detail tasks, while anything-v5-PrtRE.safetensors excels in large-scale, high-efficiency tasks. The development of the paper-cutting LoRA model not only addressed the technical complexities of generating paper-cutting patterns but also faithfully restored the cultural elements of paper-cutting, thereby contributing to the digital preservation and dissemination of intangible cultural heritage. (2) Evaluation of the LoRA Model Based on AHP and IPA Methods  
In this study, the Analytic Hierarchy Process (AHP) was first used to extract the cultural elements of paper-cutting. Through the AHP method, four core elements were objectively derived from the multi-layered cultural structure of paper-cutting art: \"Spring Festival\", \"Chinese Zodiac\", \"Women\" and \"Birds and Flowers\". These elements became the foundation for developing the Diffusion-based LoRA models, which generated eight different paper-cutting patterns based on the FLUX.1-dev and Stable Diffusion 1.5 models. Subsequently, the Importance-Performance Analysis (IPA) method was used to evaluate the developed models, providing a comprehensive assessment of the LoRA model's actual performance through the two dimensions of satisfaction and importance. After conducting both face-to-face and remote experiments, the analysis results across the four quadrants revealed user evaluations and demands regarding the various functions and features of the LoRA model. In terms of user feedback, the LoRA model received high ratings in the \"Strengths\" and \"Maintain\" quadrants, particularly in aspects such as \"The LoRA model saves time and effort\" (PE3) and \"The paper-cutting effect generated by the LoRA model is better than without it\" (PU1). Users generally regarded the LoRA model as highly practical and effective. This result demonstrates the feasibility of applying AHP and IPA methods in AI-based cultural heritage projects. At the same time, the \"Opportunities\" and \"Improvements\" quadrants in the IPA analysis provided clear directions for optimizing the LoRA model in the future.