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In the face of sparse data, high data dimensions and multi view clustering tasks, the traditional maximum entropy clustering algorithm will cause clustering failure because the class center tends to be consistent. In order to solve this problem, based on the traditional maximum entropy clustering algorithm, this paper introduced the class center punishment mechanism, integrated the weight matrix to achieve multi perspective division and integration, and constructed a multi view maximum entropy clustering algorithm with class center maximum. The algorithm reflected the importance of a certain perspective by adjusting the weight on each perspective, and solved the problem that the class center on each perspective tends to be consistent due to the scarcity of data and high data dimension in the multi perspective clustering task. Through a large number of experiments, it further proves that the clustering effect of this algorithm is significantly better than the traditional clustering algorithm when dealing with high-dimensional, sparse data, interference data participating and multi view data sets. [ABSTRACT FROM AUTHOR]

To solve the three common problems in the traditional person-post matching recommended system: data sparsiness, data cold start and low data utilization rate, this paper proposed knowledge graph-based person-post matching recommended system construction methods. By improving traditional recommendations, it integrated knowledge graph as auxiliary side information to the recommended system to carry out person-post matching recommended system, effectively solved data sparsiness and data cold start issues, and introduced knowledge graph completion algorithm to improve data utilization, the proposed method could reach 92 % in the recommended accuracy rate, and the accuracy rate was increased by about 1 % more than the existing person-post matching recommended methods. The experimental results show that the method is feasible, and the addition of the knowledge graph can improve the recommendation effect of the person-post matching recommended system. [ABSTRACT FROM AUTHOR]