Research on Intelligent Test Paper Generation Based on Multi-Variable Asymptotic Optimization

The design of question database is an important part of digitized campus construction. In this article, the current problems which has been existed in present intelligent strategies of test paper generating was analyzed, a new model of intelligent test paper was proposed. A new strategy of intelligently generation of test paper was used in this thesis. In order to identify the questions, barcode was used in this test paper generation system. The parameters were selected in sequence based on its effect on the overall constraints in test paper. This strategy can optimize the search path, improve the efficiency and flexibility of intelligently generation of test paper. The software testing was made for different problems database, then the software testing result was analyzed briefly. Practical operations show that with this approach, the searching process was optimized, the cost of time was reduced and the rate of successfully generating test papers was improved.