Museum Achieving Future Sustainable Development: A Study on the Attraction of Historical Exhibits in Museum Nursing Direction

Abstract

The aim of this study is to explore a method that combines Digital Twins (DTs) with Convolutional Neural Network (CNN) algorithms to analyze the attractiveness of historical and cultural exhibits in museums and humanistic care, in order to achieve intelligent and digital development of exhibitions under museum humanistic care. The concept of "Health Museum and Health Management" has received initial attention and rapid response from the nursing community in Europe and America. Its essence emphasizes the intervention of museum intelligence in medical and health care and the role of improving the medical and health system, creating the medical service function of museums. Firstly, using DTs technology to digitally model the historical and cultural exhibits of the museum, achieving the display and interaction of virtual exhibits. Then, the Mini_Xception network was proposed to improve the CNN algorithm and combined with the ResNet algorithm to construct a facial emotion recognition model. Finally, using this model, the attractiveness of museum historical and cultural DTs exhibits was accurately predicted by recognizing people's facial expressions. The comparative experimental results show that this recognition method can greatly improve recognition accuracy and scalability. Compared with traditional recognition methods, the recognition accuracy can be improved by 5.53%, and 2.71s can reduce the data transmission delay of the model. The scalability of enhanced recognition types can also meet real-time interaction requirements in a shorter amount of time. This study has important reference value for the digital and intelligent development of museums combined with nursing exhibitions.