Innovation and entrepreneurship education (IEE) is a powerful driving force to promote the transformation and development of college education and an effective way to broaden the employment channels. However, IEE has been used to train students in classrooms by college managers who do not systematically think of practical training for innovation and entrepreneurship talents. There are still many difficulties in the process of development of IEE that managers cannot ignore. The relevant models that could deeply analyze the connotation of IEE were established through the game theory that explained the game balance among government, universities, and students in the IEE process. A game theory model explained the external game behavior of universities and students and the internal game behavior between students. Another model analyzed the strategies of players pursuing maximum payoffs from the perspective of both sides of the game and the influences of these strategies on the players. The problems in the process of IEE development are concluded including imperfect management system, unscientific curriculum design, nonideal talent training and unprofessional teachers. Based on it, the development path of IEE is explored through the game concept. A scientific and leading system of management and evaluation is needed to put forward by strengthening the top-level design at the national level, and a win-win game situation between colleges and students will be achieved by the optimized education system.

There have been few studies on the quality assessment at the meso- or microlevel in my country’s colleges and universities despite the fact that innovation and entrepreneurship (IAE) education is constantly progressing. A new assessment method based on big data is employed in this paper to investigate the design and management of microteaching modes for IAE education and to promote the development of IAE at these institutions while also enhancing the quality of innovative talent training. This paper addresses the urgent need to reform the evaluation system for IAE education. This paper has completed the following work: (1) this paper firstly analyzes the current situation of IAE education evaluation at home and abroad on the basis of literature research and summarizes the current theoretical thinking, methods, and shortcomings of IAE education evaluation. (2) Analyzing the present condition of IAE education assessment, relevant ANN technologies are introduced, and a suitable quality index system for IAE education is created. (3) With the use of big data, we were able to gather samples and data sets for the experiment, and then, we utilized the BP algorithm and the GA-BP algorithm to compare their accuracy in predicting the quality of IAE education, and the GA-BP algorithm has better performance.

Teachers and students are the two basic elements in educational activities. Students are educated but are not exactly passive recipients of education. With subjective initiative, all educational impacts must be through the initiative of students to achieve the desired effect. Therefore, all activities of education must start from mobilizing students’ initiative and motivation so that they have sufficient motivation to learn actively and well. The effective analysis of employment data, at the statistical level of data analysis, is a favorable basis to support the influence of teachers on students. However, most of the previous methods are C4.5 algorithms, decision tree generation algorithms based on rough sets, etc., which are commonly used for employment data analysis. None of them can sufficiently deal with the problem of different decision accuracy requirements and noise adaptability. In this paper, we analyze the employment data of a university in 2012 as an example and compare the analysis results with those of the C4.5 algorithm and decision tree generation algorithm based on a rough set. The results show that the decision tree algorithm based on the multiscale rough set model generates a simple decision tree structure. In addition, our methods do not have indistinguishable datasets and are fast in terms of computing. This study provides an effective guide to the relevance of teachers’ cognitive abilities and teaching motivations for students’ employment.