In the context of sustainable ecological environment, strengthening the scientific management of cultural industry can promote the rapid development of China’s cultural industry and further promote the construction of spiritual civilization in China. Starting from culture and art management, this study expounds its position in the development of cultural economy industry and its impact on the development of cultural economy. Through two different methods of culture and art management and conventional management, this paper discusses the social and economic market benefit value, the demand for the healthy development of industry, and the impact of innovative cultural industry resources on the development of cultural industry. It can be concluded that culture and art management plays a certain role in promoting the healthy development of cultural economy industry; we should constantly sum up experience in the actual process of cultural industry management, find and solve problems in time, and gradually form a set of scientific and complete cultural and art management mode, which can realize the transformation of cultural and art industry faster and better promote the sustainable development of cultural economy and industry.
With the continuous improvement of computer software and hardware performance, a large amount of image and video data can be easily collected and quickly transmitted, and new recognition methods that introduce deep learning are emerging, making the application and research of face recognition technology. The value is also increasingly prominent. The purpose of this paper is to study the face recognition robot implementation algorithm based on deep learning. The research background and significance of face recognition and expression recognition, which are the core of facial biological information extraction, are introduced. The face feature extraction network structure of Inception-ResNet-V1 has been improved, and high recognition features of faces can be obtained. At the same time, the training of the feature extraction model of the self-built training set and the adjustment of hyperparameters are completed. Finally, the effectiveness of the improved network in this paper is fully verified in the LFW test set and the actual robot environment. It is verified by experiments that the proposed optimization method can improve the performance of the network. It also verified the significant research significance of the current deep learning direction through practice.
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