Introduction. The development of network technology is promoting the process of digitalization. The digital museum, an emerging museum display mode, is gradually highlighting its great value in the wave of digitalization. In the context of the rapid development of digital museums and mobile applications, the user interface of digital museums, as the integration point of human-computer interaction, the artistic expression of its visual design is also more important. This paper takes the digital museum application (APP) user interface design as the research direction. By optimizing the visual design of the interface, the users operating experience is improved, so that users can enjoy an orderly, time-saving, efficient, comfortable, and interesting interactive experience. Methods. User interface design to analyze related theories such as target-oriented design and visual hierarchy design and dig out the manifestation of user goals in the museums APP visual hierarchy and clarify the design content. Use qualitative and quantitative user research methods to conduct demand research for museum users and build user role models. Build user role target task model. Determine the function settings of the museums APP and establish an information structure. Secondly, build a low-fidelity model through the level analysis of the visual elements in the interface and use the low-fidelity prototype test to guide the museums APP visual level design. The interface visual level elements are integrated into the museums APP visual level framework and integrated with the museums characteristics Together, put forward the visual hierarchical design strategy of the museum APP. Results. A new model for the dissemination and display of cultural information resources by transposing the display of museum information content fusion to the works of the digital platform. The ease of use, fun, and artistic quality of the user interface of the digital museum are the keys to attracting users. Through the summary of the theory and the construction of the design strategy, we build and design a museum APP that meets the users experience, meets the users goals, and has a good visual hierarchy. The experimental results show that the largest number of people, 62.6, wanted to learn about local culture by visiting museums. The number of people whose purpose was to travel and the study was the next highest, and they accounted for an equal share, 43.1 and 40.6, respectively. A smaller number, 23.4, attended museums for hobbies and interests. The number of people who visit museums for research purposes is even lower, at 11.9. Through usability testing and user satisfaction analysis of the design model, the rationality and effectiveness of the design strategy are verified, which can provide appropriate guidance for museum app design. Conclusion. The problems encountered in the visual design and production of digital museums and the solutions were discussed, and through a comparative analysis with other digital museum visual designs, it provided theoretical and practical basis for the construction and research of digital museum visual design in the future. Our digital museum user interface design is based on user needs and solves the previous problem of users not understanding how to use the museum app effectively.

Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics. Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filters. An electroencephalogram (EEG) experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data were recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a Support Vector Machines (SVMs) was utilized to model the feature data, Event-Related Potentials (ERPs), context data, and subjective aesthetic evaluation data. Result: Behavioral data revealed higher aesthetic ratings in the genuine context. ERP data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200 and 1000 ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50–120 ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200–300 ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC = 0.76866) outperforms the model using only parameters of the aesthetic object (ACC = 0.68657). Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics.

Color language has important meaning for children’s picture books. Understanding children’s preferences in terms of color language will be helpful when designers model computerized picture books in order to adapt to children’s visual senses and effectively stimulate children’s interest in reading. In this study, we aimed to further explore the general characteristics of children’s preference for colors, color depth and color matching forms in picture books. For the study, 256 children between the ages of three and six were selected and divided into four groups. According to the implementation needs of the children’s color preference test, these children were asked to complete a series of tasks so that their choices in the tasks could subsequently be analyzed. The tasks included making the following choices: selecting their preferred colors from nine options (red, orange, yellow, green, cyan, blue, purple, black and white); selecting their preferred color brightness from three different color shades; and indicating their preferences in relation to complementary colors, matching colors and pure colors. The experimental results showed that children aged between three and six mainly favor red, blue and yellow, and colors with a high degree of brightness. As children grow older, boys tend to prefer blue, and girls tend to prefer pink, with red remaining the favorite color of both genders. As for complementary colors, matching colors and solid colors, three- to six-year-old children prefer solid colors. In conclusion, it seems that color language preferences in children between the ages of three and six are very similar, which could be of use when designing children’s picture books to match their visual vocabulary and psychological cognition.