Under the background of the rapid development of artificial intelligence, intelligent natural language processing technology has made rapid development. Different from the question answering system in other fields, the establishment of electric power Intelligent Customer Service System (ICSS) makes the construction of intelligent question answering system more challenging. This paper discusses and analyzes the design and implementation of intelligent question and answer system, the demand analysis of power ICSS and the construction process of address and place database. Through the function, performance and security test of power intelligent customer service, the test results show that the version of the release system runs stably and has been actually applied in the management of power customer service. After the launch of the power CSS, customers generally reflect that the effect is very good. They agree that the promotion of the power CSS in the power industry is an extremely important basic project to realize the hierarchical requirements of standardized management and operation.
In recent years, due to the continuous advancement of computer software and hardware and the advancement of artificial intelligence, the advancement of human-machine communication systems has made considerable progress. The natural language understanding module is the basic element of the human-computer communication system, and the result of its semantic understanding will have a significant impact on the development of later components, and directly affect the process and the improvement of the success rate of human-computer interaction. In this paper, the neural network-oriented human-computer interaction-oriented natural language understanding and interaction engine is researched. On the basis of literature data, the relevant knowledge of human-computer interaction is understood, and then the neural network-oriented human-computer interaction natural language understanding is designed on the interactive engine, and the neural network reasoning structure model it quotes is tested. The test results show that the RMNN model used in this paper has achieved an accuracy of 70.24%, and the significance test p-value is 0.001.
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