In the field of deep learning, for problems and tasks that are sensitive to time series, such as natural language processing or speech recognition, the recurrent neural network is usually more suitable. Long short-term memory (LSTM) is a representative network structure in recurrent neural network. It is time-dependent and enables a global representation of features. However, some problems such as the network parameters of LSTMs limit the applicability of their solutions. This paper proposes an improved hybrid structure of graph convolutional neural network and recurrent neural network. In the input layer, a two-dimensional convolutional neural network is used to build a text corpus map. Graphic embedding is used to preserve the global structure of the entire text graph structures. The LSTM layer and attention mechanism are used to fully implement text classification and improve the computational efficiency. The test results show that the hybrid network structure has better operation speed on the IMDb dataset.
With the rapid development of computer technology and Internet, the traditional data mining methods and technologies in power industry will face great difficulties, and it is difficult to carry out accurate data processing and analysis. How to mine valuable data from a large number of original data has become a research difficulty. Aiming at this problem, this paper establishes the framework design of power enterprise central data platform based on big data. In order to further improve the actual performance of the scheme, the defects of existing algorithms are analyzed by IM_Apriori improves the calculation method, simplifies the calculation steps, reduces the calculation times, and provides technical support for enterprise data analysis. Through the analysis of the test results, when the data peak reaches 100 m, the execution time is reduced by 25s, which is obviously superior to the traditional scheme. The test results show that the design scheme in this paper has a high comprehensive performance, compared with the traditional central data platform framework, the performance has been greatly improved. Through the analysis, the research in this paper has achieved ideal results, and has made a contribution to the research on the framework design of the central data platform of power enterprises.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.