# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 3 Higher Education Evaluation System  
### Design of Evaluation System  
The first step is to choose indicators which summarize a national higher education system. In the process of indicator selection, we investigate a large number of educational evaluations at home and abroad, with reference to the indicator system and the content of national undergraduate teaching evaluation, as well.  
According to the context, input, process, product (CIPP) model proposed by [PERSON] et al. [22], we summarize the main contents of the development level of higher education as follows:  
* Population, social and economic background of higher education: To understand the structure, process and achievements of higher education and various relationships between them, we first need to examine the basic conditions on which national higher education system relies. These conditions mainly include the situation of the population and social and economic development because these conditions restrict and affect the formulation and implementation of relevant higher education policies and affect the supply and demand of colleges and universities, teachers, teaching facilities and other higher education resources.
* Financial and human resources invested in higher education: These kinds of indicators mainly investigate the proportion of higher education institutions in national resources, the source of funds and the level of higher education used, as well as the number and proportion of all personnel employed in higher education departments. At the same time, these indicators also examine how financial funds for higher education are transformed to help students in their studies and scientific research.
* Access to higher education, participation and further study: Such indicators mainly include: (1) the participation rate of senior secondary education; (2) the participation rate of higher education (including public and private universities); (3) the participation in adult education; (4) the ratio of foreign students receiving higher education in China and Chinese students receiving higher education in foreign countries (the difference between exit and entry); (5) the type and proportion of adult workers participating in continuing education and training; (6) the dropout rate of private college students; and so on. The reason for adding the indicator is that international student mobility involves the economic expenditures and incomes of sending and receiving countries, as well as the corresponding brain drain.
* Social output and scientific output: These kinds of indicators mainly investigate the labor market output of higher education to the whole working age population, as well as the achievements of higher education in scientific research. These are: (1) the unemployment rate of students in higher education at all levels; (2) the proportion of employees receiving higher education; (3) the number of papers published; and (4) the number of patents published.  
Based on the above research, we take the following four first-level indicators to describe higher education evaluation: higher education foundation, higher education investment, higher education process and higher education achievement. For the indicators that cannot be directly quantified, we use the secondary indicators represented by data to describe them more accurately. Referring to the two-stage semiparametric data envelopment analysis (DEA) model proposed by [PERSON] [23], we finally establish 12 second-level indicators. The framework of the evaluation model is shown in Figure 1.