At present, there are many research results in Tang poems and Song lyrics from the perspective of literature, but there are few research results in Tang poems and Song lyrics by using the hypergraph based hypernetwork method, and the only researches of this kind are also limited to the study of Chinese character frequency and word frequency. The analysis and study of Tang poems and Song lyrics by using the method of hypernetwork data analysis is helpful to explore the breadth that cannot be reached by the traditional perspective of literature, and to discover the law of word composition laws in literatures and historical backgrounds reflected by Tang poems and Song lyrics. Therefore, based on two ancient text corpuses:Quan Tang Shi and Quan Song Ci, the hypernetworks of Tang poems and Song lyrics were established respectively. In the construction of the hypernetworks, a Tang poem or a Song lyrics was taken as a hyperedge, and the characters in Tang poems or Song lyrics were taken as the nodes within the hyperedge. Then, the topological indexes and network characteristics of the hypernetworks of Tang poems and Song lyrics, such as node hyperdegree, node hyperdegree distribution, hyperedge node degree, and hyperedge node degree distribution, were experimentally analyzed, in order to find out the characters use, word use and aesthetic tendency of poets in Tang dynasty and lyricists in Song dynasty. Finally, based on the works of poems and lyrics of Li Bai, Du Fu, Su Shi and Xin Qiji, the work hypernetworks were constructed, and the relevant network parameters were calculated. The analysis results show that there is a great difference between the maximum and minimum hyperdegrees of the two hypernetwork, and the distribution of the hyperdegrees is approximate to the power-law distribution, which indicates the scale-free property of the two hypernetworks. In addition, the degrees of hyperedge nodes in Tang poem hypernetwork are also have obvious distribution characteristics. In specific, the degrees of hyperedge nodes in Tang poems and Song lyrics are more distributed between 20 and 100, and the degrees of hyperedge nodes in Song lyric hypernetwork are more distributed between 30 and 130. Moreover, it is found that the work hypernetworks have smaller average path length and a larger clustering coefficient, which reflects the small-world characteristics of the work hypernetworks.

Most of the existing link prediction algorithms only study the first-order similarity between nodes and their neighbor nodes, without considering the high-order similarity between nodes and the neighbor nodes of their neighbor nodes. In order to solve this problem, a Link Prediction algorithm based on High-Order Proximity Approximation (LP-HOPA) was proposed. Firstly, the normalized adjacency matrix and similarity matrix of a network were solved. Secondly, the similarity matrix was decomposed by the method of matrix decomposition, and the representation vectors of the network nodes and their contexts were obtained. Thirdly, the original similarity matrix was high-order optimized by using Network Embedding Update (NEU) algorithm of high-order network representation learning, and the higher-order similarity matrix representation was calculated by using the normalized adjacency matrix. Finally, a large number of experiments were carried out on four real datasets. Experiments results show that, compared with the original link prediction algorithm, the accuracy of most of the link prediction algorithms optimized by LP-HOPA is improved by 4% to 50%. In addition, LP-HOPA can transform the link prediction algorithm based on local structure information of low-order network into the link prediction algorithm based on high-order characteristics of nodes, which confirms the validity and feasibility of the link prediction algorithm based on high order proximity approximation to a certain extent.

Question classification is one of the tasks in question answering system. Since questions often have rare words and colloquial expressions, especially in the application of voice interaction, the traditional text classifications perform poorly in short question classification. Thus a short question classification algorithm was proposed, which was based on semantic extensions and used the search engine to extend knowledge for short questions, the question's category was got by selecting features with the topic model and calculating the word similarity. The experimental results show that the proposed method can get F-measure value of 0.713 in a set of 1365 real problems, which is higher than that of Support Vector Machine (SVM), K-Nearest Neighbor (KNN) algorithm and maximum entropy algorithm. Therefore, the accuracy of the question classification can be improved by above method in question answering system.