# 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
However, under the limited conditions, such as the determination of the scale of college students, the impact of education policy on the total investment in higher education is negative. For instance, due to the impact of a series of major education policies promulgated and implemented in 1993, investment in higher education increased by an average of CNY 1 million under the condition that the economic aggregate remained unchanged and decreased by an average of CNY 40 million when the number of college students remained unchanged. In addition, after the enrollment expansion policy was implemented in 1999, the investment in higher education increased by an average of CNY 2 million under the condition that the economic aggregate remained unchanged, When the number of college students remains unchanged, the average decrease is CNY 5 million. Inspired by this paper, we define the impact of policies on different indicators. According to our model, when policies change, it impacts the prediction of the original series periodically. We assume the impact factor of policy on each indicator as \(rate\). Obviously, \(rate\) is sensitive to different indicators. In China, since the promulgation and implementation of policies generally take five years as a cycle, the \(rate\) value of each cycle is the same. Combining with China's specific national conditions, the values of \(rate\) for each indicator in different cycles are shown in Table 8.  
Figure 7 shows the specific process of the optimized prediction model. We import the original data into the trained LSTM neural network to predict the time series in five years. Then, the result is multiplied by \(rate\) and incorporated into the original sequence. Then, the new sequence is put into the model for the next round of prediction, and the final results are obtained through iteration. The calculation formula is as follows. \[Net=TrianNet(XTrain,YTrain) \tag{19}\]  
\[YPred=PredictAndUpdate(Net,YTrain) \tag{20}\]  
\[Data=Data+YPred\times rate \tag{21}\] \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **2018–2023** & **2024–2028** & **2029–2033** & **2034–2038** \\ \hline GDP per capita & 0.6 & 0.5 & 0.3 & 0.3 \\ Gross enrollment rate & 0.25 & 0.2 & 0.1 & 0.1 \\ Proportion of undergraduates & 1 & 3 & 1 & 1 \\ Proportion of masters and doctors & 4 & 6 & 3 & 3 \\ International student ratio & 0.5 & 0.6 & 0.7 & 1 \\ Student-teacher ratio & \(-\)0.05 & \(-\)0.05 & \(-\)0.1 & \(-\)0.05 \\ Ratio of R\&D to GDP & 0.2 & 0.2 & 0.1 & 0.1 \\ Proportion of higher education in & 0.3 & 0.3 & 0.2 & 0.2 \\ Number of SCI papers published & 0.05 & 0.05 & 0.1 & 0.05 \\ Number of patents & 0.02 & 0.02 & 0.05 & 0.02 \\ Proportion of employed persons & 0.5 & 0.8 & 0.3 & 0.3 \\ receiving higher education & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The corresponding values of rate in different cycles. In Formulas (19)-(21), \(Net\) represents the LSTM neural network. \(XTrain\) and \(YTrain\) represent the data series. \(TrainNet\) represents the function of training the LSTM neural network. \(PredictAndUpdate\) represents the function of predicting and updating the LSTM neural network. \(YPred\) represents the prediction result of the model. \(Data\) represents the optimized output of the model. The value of \(Data\) is renewed according to the product of \(Ypred\) and \(rate\).