Water affair data mainly consists of structured data and unstructured data, and the storage methods of data are diverse and heterogeneous. To meet the needs of water affair information integration, a method of constructing a knowledge graph using a combination of water affair structured and unstructured data is proposed. To meet the needs of a water information search, an information recommendation system for constructing a water affair knowledge graph is proposed. In this paper, the edit distance algorithm and latent Dirichlet allocation (LDA) algorithm are used to construct a water affair structured data and unstructured data combination knowledge graph, and this graph is validated based on the semantic distance algorithm. Finally, this paper uses the recall rate, accuracy rate, and F comprehensive results to compare the algorithms. The evaluation results of the edit distance algorithm and the LDA algorithm exceed 90%, which is greater than the comparison algorithm, thus confirming the validity and accuracy of the construction of a water affair knowledge graph. Furthermore, a set of water affair verification sets is used to verify the recommendation method, which proves the effectiveness of the recommended method.

With the coming of the 5G network era, Quality of Experience (QoE), which is used to describe experience quality of customers in the Direct Connect (DC) network, is critical to operators and hence has been widely studied. The existing evaluation method for QoE is unable to dynamically adjust QoE parameters so there will be deviations to reflect the real experience of customers when complaint events occur. In this paper, a novel progressive QoE evaluation model for network products is formulated. Subsequently, considering the customer complaints occurrences which represent that the real customer experience and previously calculated QoE may mismatch, the rules based on application types to judge whether QoE parameters needs to be adjusted are made. Then, a dynamic adjustment model of QoE parameters is further developed to reflect complaint customer experience. Finally, the obtained experimental result verified that the proposed dynamic perception method for QoE can accurately characterize the real customer experience. The evaluation result for QoE model based on customer satisfaction questionnaires proves the effectiveness of the QoE-aware model. The evaluation result for adjusted QoE model based on business complaints shows that the adjusted QoE calculation variance reduction rate exceeds more than 50%, which indicates that the proposed method has better ability to reflect the real customer experience.