# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 1 Introduction
[9] proposed a feature-dependent naive Bayes (FDNB) classification method by following preprocessing steps. Their method was applied to problems of software defect prediction, and experiments were carried out using NASA's dataset. Their results showed that this new method is more successful than the standard naive Bayes method and has competitive performance with other functional weighting techniques. Their study also proved the importance of training data when building machine learning models. The ideas in these studies provide us with a good reference for constructing models. However, for prediction of higher education levels, it is necessary to simultaneously take into account the influences of multiple indicators and parameters. When dealing with long series of data, these prediction methods have certain disadvantages. For example, a Support Vector Machine (SVM) is more sensitive to choices of parameters and kernel functions, while the original SVM is only suitable for handling two-class classification problems. In addition, regardless of whether the Markov chain is faulty or repaired, the probability of change in the hypothetical state is fixed. At the same time, all states are statistically independent, so future states are independent of all past states. Thus, Markov is not suitable for medium- and long-term predictions. In addition, grey forecast is only suitable for short- and medium-term prediction, as well, and it is only applied to prediction with approximate exponential growth. Moreover, the accuracy of the naive Bayes model largely depends on its prior probability, and the prior probability often depends on its hypothesis. There can be many types of hypothetical models and thus cases in which accuracy is very much subject to assumptions. To address this, this paper makes two main contributions. Firstly, we establish a comprehensive evaluation model. Based on the four aspects of basis, quality, development and achievement, higher education is divided into four first-level indicators and then further divided into 12 second level indicators for a more detailed description. In this way, we build an overall framework of our evaluation model. Then, we introduce the analytic hierarchy process (AHP), express the relative importance of the indicators with numbers, and calculate a weight matrix of the entire evaluation model. Next, we determine eight representative countries as our research objects and obtain the corresponding education data in authoritative datasets. In addition, we input the data and weight matrix into the technique for order preference by similarity to an ideal solution (TOPSIS) algorithm to calculate a higher education level for each research object. The second contribution of our paper is to add a predictive function to our evaluation model. In order to make the model better fit reality, we introduce a long short-term memory (LSTM) neural network to establish a prediction model. In LSTM, there is a memory effect on input data, and it can screen or modify the data through a specific equation. Moreover, problems of gradient explosion and gradient dispersion in typical recurrent neural networks (RNNs) are addressed. LSTM has the following advantages: first, there is no need to calculate time series parameters in advance; second, there are not too many requirements on the nature of time series data, and its scope of application is wide; third, it can learn the complicated rules of time series, and it is not just mechanically targeting certain fixed factors but also can be used as a complex nonlinear unit to construct larger deep neural networks. Finally, we set up two environments, namely, a free development environment without policy intervention and a macro control environment with policy intervention. We design effective policies and adjust parameters of LSTM according to its environments. Then, we compare the model results to verify whether a policy has significantly improved levels of higher education.