# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 4 Higher Education Prediction Model
### Circumstance Setting
#### 4.5.2 Circumstances with Policies  
When considering circumstances with policies' influence, we assume that the policies have a direct impact on various indicators of the evaluation model, and according to the pertinence of the policy, various indicators are affected in different degrees. On the one hand, if the main point of the policy is to develop the economy, then the indicators related to higher education investment will be significantly improved. Relatively speaking, the change in the teacher-student ratio may not be so noticeable. On the other hand, if the policy emphasizes enhancing international exchanges, the proportion of international students will also increase, while the impact on other indicators will be limited. In general, a country's higher education is closely related to its economic level, talent training mode and educational resources. At the same time, higher education is also the key driving force of national innovation, technological breakthroughs and industrial upgrading. In other words, the standard of higher education is closely related to the future of the country. At the same time, because higher education involves all aspects of society, the reform of higher education needs to started from multiple directions, which is a gradual process. Particularly for China, due to its population base, the opportunities provided by the current higher education system are relatively limited, resulting in violent involution. Thus, it is vital to innovate and create a higher education system and enrich educational resources. * Specific Policies and Timeline Considering that the national policies are not static, we assume that the policies are adjusted once every 5 years, and the current higher education system is evaluated periodically accordingly. While implementing the policies, we focus on the evaluation of China's higher education. According to the model prediction results without policy influence, we design the policies and timeline as shown in Table 7. The timeline describes the key improvement indicators and corresponding policies every 5 years. Moreover, the timeline only represents the main implementation time, not the end time, of the policies. \begin{table}
\begin{tabular}{c c} \hline \hline Number of hidden layers & 200 \\ \hline Loss function & MSE \\ \hline Learning rate & 0.005 \\ \hline Gradient threshold & 1 \\ \hline Optimizer & Adam \\ \hline Maximum training rounds & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Set of Parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **2019–2023** & **2024–2028** & **2029–2033** & **2034–2038** \\ \hline \multirow{4}{*}{Important indicators} & GDP per capita & \multirow{4}{*}{Proportion of undergraduates Ratio of R\&R to GDP} & \multirow{4}{*}{Proportion of masters and doctors} & Student teacher ratio \\  & & & & Number of SCI papers Number of patents \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & Develop hard economy & \multirow{4}{*}{Extuate the ability of innovation and pay attention to research talents School and enterprise developing} & \multirow{4}{*}{Shsorb high-quality talents and search for technological innovation} & \multirow{4}{*}{Broaden international horizon and improve the treatment of students treatment of students} \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Policies and timeline. The details are illustrated as follows. * 2019-2023: Build a good foundation _(1) Strongly develop the economy and build a good foundation of education_: According to the poverty line standard established by the world bank, as an upper-middle-income country, the number of poor people in China is calculated according to the standard of 5.50 international dollars per day.