Supergroup algorithm and knowledge graph construction in museum digital display platform

In response to the problems of low entity recognition accuracy, low user satisfaction, and weak interactivity in the construction of knowledge graph for digital display of museum cultural relics, this article studied the application of supergroup algorithms and knowledge graph construction in museum digital display platforms to solve the existing problems. By utilizing the K-means algorithm in the supergroup algorithm to conduct a survey of visitors to Museum A and analyze the behavior of 180 selected visitors, the display effect and audience satisfaction can be improved. Various knowledge graph technologies were utilized to construct a knowledge graph of museum cultural relics. Various knowledge resources in museums were associated and integrated, and through the collection and processing of museum cultural relic data, cultural relic ontology construction and relationship extraction were achieved, providing viewers with richer and more in-depth display content. Through experiments, it was found that the visitor satisfaction rate based on the K-means algorithm was above 92.68 %, and the average visitor satisfaction rate after 10 experiments was 94.25 %. The accuracy, recall, and F1 values of the museum cultural relics knowledge graph studied in this article were 90.12 %, 84.69 %, and 82.23 %, respectively, which were much higher than other types of knowledge graphs. By applying these advanced technologies to the digital display platform of museums, not only can the visitor experience be improved, but also the digitalization process of museums can be promoted, contributing to cultural dissemination and development.