Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.

Big data technology has greatly promoted the construction of intelligent administrative management and improved the decision-making ability continuously. Data mining also lays a solid foundation for the construction of administrative management platform and reflects the potential value of data. In this study, an intelligent management platform based on big data is designed and implemented. First, the problems of the intellectualization of administrative management are discussed, and the big data platform and functional framework of administrative management are introduced. Second, in order to apply data mining to administrative management, the object of data mining in administrative management is defined, and a data mining system is designed. Finally, the application of machine learning methods such as cluster analysis in administrative management is analyzed in detail. The research results show that the application of intelligent management platform based on big data can promote the construction of intelligent administration and lay a good foundation for the development of more perfect intelligent administrative management.