A Study of Digitization Strategies and Audience Interaction in New Media Art Exhibitions in Museums

Abstract

Abstract With the rapid development of digital technology, the digital system based on the fuzzy kano model provides a digital strategy for new media art exhibition museum information dissemination, digital display, and other fields. In this paper, the fuzzy Kano model is used to design the digital museum, and the system design starts from the five elements of user experience. The digital system is designed from the levels of the strategy layer, scope layer, structural layer, framework layer, and performance layer, respectively. Clustering the sensors first is used to obtain the functional area in the classification of audience behavior. The optimal clustering results can be achieved through spectral clustering of sensor graphs. Clustering is proposed using the typical movement pattern extraction algorithm. Optimization indexes are set to achieve optimal typical movement patterns. The auxiliary sensors capture the interactive movements of the audience with the artwork and collect logical information. Behavioral pattern templates for various types of viewers are constructed using viewer attribute labels. In the user classification method based on behavioral patterns, edges in the graph are used as features, and an optimization problem is constructed to solve the importance of each feature for audience classification. The implementation of interactive features greatly enhances the interactive experience of the audience. It makes the audience’s novelty evaluation score of the system reach 8.671. The classification algorithm based on the behavioral model performs well in all evaluation indexes, which indicates that the system proposed in this paper meets the audience’s digital and interactive needs for new media art exhibitions.