SummaryWith the development of network technology, people are facing more and more massive information. How to extract emotional information in massive information rapidly has received more and more attention from people. This paper introduces the principle and structure of the traditional emotional model. Different personality, emotional states, and external stimuli will have different effects on emotional semantic analysis. In addition, this paper has proposed emotional semantic analysis method based on wake‐sleep and SVM method. The model starts from the description and calculation of the dynamic characteristics of emotions and more fully predicts the process characteristics that describe the evolution of emotions. Search and category browsing allows users to quickly access these information points. In addition, this paper provides a deep learning fusion algorithm in emotional semantic analysis, introduces its reference implementation and related key technologies, and supports business intelligence to a certain extent, and it has a strong application prospect on the network data information.

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In this paper, we propose a Genetic Network Programming (GNP) based ranking method to improve the accuracy of Classification Based on Association Rule(CBA). We start from an empirical phenomenon, that is, the accuracy could be improved by changing the ranking of rules in CBA. Then, we apply GNP to build a model, namely RuleRank, to find good ranking equations to rank association rules in CBA. The simulation results show that RuleRank could improve the accuracy of CBA effectively.

Police Patrol is a very useful method for managing the social security. Many researches focus on how to obtain the best strategy for police patrol. We got this research topic from The Sixth National Post-Graduate Mathematical Contest in Modeling. In the proposed approach, Greedy Policy and Randomized Strategy are combined with Genetic Algorithm to solve these problems, simulation result shows the assignment and patrol strategy for police cars have a good effect with great concealment.