Research on hotel customer satisfaction prediction and intelligent service management model based on deep neural network

With the rapid expansion of the Internet and e-commerce, and the rapid revolution of the consumption mode, customer reviews have become the most important feedback means for customers' preference and satisfaction level of products nowadays.In this paper, hotel customer reviews are used as the basis for predicting hotel customer satisfaction, and the TF-IDF feature word extraction method is proposed to extract review feature words.Based on deep neural network, we propose the sentiment analysis technology of hotel customer reviews, use BERT neural network to construct aspect term extraction model, realize the sentiment recognition and quantification of hotel customer reviews, and combine the fuzzy comprehensive evaluation and IPA analysis as the prediction and analysis model of hotel customer satisfaction.Taking 25837 customer reviews of XC Hotel as a research sample, we explore and analyze the satisfaction of XC Hotel customers.The secondary and primary features of the reviews were extracted by review feature words, and the themes were extracted by LDA theme mining model, which concluded that the evaluation items of concern for XC Hotel lie in location, facilities, hygiene, service, price, and food and beverage.The prediction results showed that 69.59% of customers were satisfied, 18.42% felt average and 11.99% were dissatisfied.IPA analysis of satisfaction and importance of XC Hotel and its visualization were conducted, and the intelligent service management model of the hotel was constructed based on the results of IPA analysis, and the optimization strategy of intelligent service of the hotel was proposed.