Mobile social network data has complex network structure, mutual label influence between nodes, variety of information including interactive information, location information, and other complex information. As a result, it brings many challenges to identify the characteristics of the user. In response to these challenges, a real mobile network was studied, the differences between the tagged users with different characteristics were extracted using statistical analysis, then the user's features of age and sex were recognized using relational Markov network prediction model. Analysis shows that the user of different age and sex has significant difference in call probability at different times, call entropy, distribution and discreteness of location information, gather degree in social networks, as well as binary and ternary interaction frequency. With these features, an approach for inferring the user's age and gender was put forward, which used the binary and ternary interaction relation group template, combined with the user's own temporal and spatial characteristics, and calculated the total joint probability distribution by relational Markov network. The experimental results show that the prediction accuracy of the proposed recognition model is at least 8% higher compared to the traditional classification methods, such as C4.5 decision tree, random forest, Logistic regression and Naive Bayes.

Multi-constraint constrains and reduces the test success rate, and it is difficult to make the knowledge points uniformly and automatically distributed in intelligent generating test paper. To solve these above problems, a multi-constraint hierarchical optimization strategy was proposed. It used hierarchical method to reduce the problem size, and used the tree structure to manage knowledge points and realize uniform distribution of knowledge points. With regard to the low success rate and efficiency of small test bank in generating test paper, a forecast calculation algorithm without backtracking was put forward based on hierarchical optimization algorithm to increase the test success rate. The experimental results indicate that the algorithm is suitable for large, medium and small question database, and all of them have good results.