## 2 Literature Review  
In recent years, the rapid development of digital technologies and intelligent algorithms has provided more scientific and efficient support for the protection and transmission of cultural heritage. This review focuses on the role of color network models, interactive genetic algorithms (IGA), and color harmony prediction models in the color analysis and design applications of cultural heritage. Firstly, color network models are widely used in the extraction and composition analysis of color features in cultural heritage. For example, [PERSON] et al. [8] used a color network model to construct the color network of She ethnic clothing. By extracting color proportions, binary color co-occurrence rates, and other color features, they demonstrated the color differences in garments from different regions, presenting the color composition intuitively. However, this model faces challenges in large-scale regeneration design due to its low degree of automation, making it difficult to meet the demands of efficient design. To improve design efficiency, [PERSON] et al. [9] combined the color network model with IGA for the regeneration design of Dunhuang colors. Although this method performed well in color feature regeneration and innovation, the traditional IGA it employed still relied on manual evaluation [10; 11; 12], which can lead to user fatigue and affect efficiency. To address this issue, some researchers have introduced intelligent algorithms as proxy evaluation mechanisms within IGA to replace some manual evaluations, effectively enhancing the automation level. For example, [PERSON] et al. [13] proposed a clothing customization platform that integrates typical styles with IGA, significantly reducing user fatigue. [PERSON] et al. [14] used an aesthetic measurement formula to evaluate color quality in IGA and constructed an automatic color evaluation model for Hanfu, enabling the rapid generation of color schemes. However, the model remains simple, and the evaluation effect is still insufficient. The emergence of machine learning-based color harmony prediction models has provided new perspectives for the automation of color evaluation. Notably, [PERSON] et al. [15] collected a large dataset of five-color palettes from online platforms such as Adobe Kuler and COLOURLovers and utilized Amazon Mechanical Turk (\"MTurk\") to create a dataset for predicting color harmony. They extracted color features through a data driven approach, [PERSON]'s color wheel templates, and color harmony theory, training a five-color harmony prediction model that achieved a prediction accuracy of 56%. Building on [PERSON] et al. 's research, [PERSON] et al. [16] further refined the analysis by reducing the number of features from 334 to 118 and trained a multivariate regression model capable of predicting the harmony of arbitrary color combinations, achieving a prediction accuracy of 52%. [PERSON] et al. [17] proposed a color theme evaluation method combining a backpropagation neural network with a kernel probability model, enhancing the automation of color harmony assessment. While these models have significantly contributed to automating color evaluation, there remains room for improvement in prediction accuracy, and their applications in cultural heritage color regeneration remain underexplored. In summary, the color network model effectively extracts the color characteristics of cultural heritage but remains insufficient for fully automated design. Traditional interactive genetic algorithms (IGA) can be applied to color regeneration but suffer from user fatigue due to their reliance on manual evaluation, ultimately affecting efficiency. In recent years, researchers have introduced color harmony prediction models to assist evaluation through intelligent algorithms. However, these models still require further improvement and have yet to form a comprehensive framework for precise color regeneration in cultural heritage applications.