# Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Authors: Xing Li; Ting Yang
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

Scientific and reasonable forecast model of graduates’ employment data can efficaciously embody the complex characteristics of graduates’ employment data and embody the nonlinear dynamic interaction of influencing elements of graduates’ employment situation. It has a strong and steady characteristic learning capability, thus selecting the main influence data that influence the change of graduates’ employment data. In this paper, according to the situation embodied by students’ employment, a data mining analysis model is set up by using the statistical method based on the model of cluster analysis technology to forecast the employment situation of graduates. In this paper, a forecast technique of graduates’ employment situation based on the long short-term memory (LSTM) recurrent neural network is conceived, including network structure design, network training, and forecast process implementation algorithm. In addition, aiming at minimizing the forecasting error, an LSTM forecasting model parameter optimization algorithm based on multilayer grid search is conceived. It also verifies the applicability and correctness of the LSTM forecasting model and its parameter optimization algorithm in the analysis of graduates’ employment situation.


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## Body

## 1. Introduction

With the rapid progress of related technology in the computer industry, its use scope has expanded to all levels of modern social life, even to many fields of national daily life [1]. With the fast progress of data bank technology and the extensive use of data bank supervisory system, people amass more and more data, and there are many critical data concealed behind a large number of data [2]. Only with sophisticated data bank technology can we efficaciously manage the vast amount of data and select helpful information for ourselves [3]. Data mining is a data analysis method, which analyzes the observed data sets, finds the relationships and rules in the data, and describes the general characteristics of the data in the data bank [4]. In order to forecast the current data, people can use the computer to get forecast information. Data mining technology can automatically analyze data, carry out summary reasoning and association on them, find some internal association between data, and find potential patterns that play a very important role in information forecast and decision-making behavior, so as to set up new models, which provide great help for people's correct decision-making [5]. Graduates' employment data not only is influenced by many elements, but also has complex nonlinear dynamic interaction among them [6]. Then the data of graduates' employment situation becomes a complex system with the characteristics of sequence correlation, nonstationary, and nonlinear. According to the employment situation of a large number of graduates, the matter of forecasting whether a graduate can be employed is actually a data mining problem [7].Data mining is a new technology to find and select cryptic information from data warehouse. It is based on the data bank and data warehouse, facing nonprofessional users, positioning on the desktop, and supporting impromptu random query [8]. Building a scientific and effective forecast model of graduates' employment situation has become a challenging work with important academic and application value. Efficaciously forecasting the employment situation after graduation has important guiding significance for the rational development of reliability plan and the development of reliability education activities [9]. According to the situation embodied by students' employment, this paper uses the statistical method based on model in cluster analysis technology to set up a data mining analysis model to forecast the employment situation of graduates. In this paper, a graduate employment prediction method based on long short-term memory (LSTM) recurrent neural network is proposed, including network structure design, network training, and prediction process algorithm. In addition, aiming at minimizing the prediction error, it is verified that the LSTM prediction model and its parameter optimization algorithm have strong applicability and high correctness in graduate employment analysis [10].Chen and Ren [11] put forward that a scientific and effective prediction model of graduates' employment data can fully reflect the complex characteristics of graduates' employment data, such as nonlinearity, nonstationarity, and series correlation, and can effectively reflect the nonlinear dynamic interaction of influencing factors of graduates' employment situation. Thirdly, it has strong and robust feature learning ability, so as to extract important influence information that affects the changes of graduates' employment data. Rasjid et al. [12] showed that feature fusion in multicycle scale is an important analysis method in employment situation prediction research, but most employment situation prediction methods using LSTM neural network often only extract features for a specific cycle, ignoring the information of employment situation in other time scales. Dong et al. [13] pointed out that the credibility of class label is the basis of the classification prediction of graduates' employment situation, and its setting will directly affect the internal correlation between input data and output data. It can be seen from [14] that LSTM neural network has higher prediction accuracy and can effectively predict the long-term and short-term dynamic trends of graduates' employment data, which shows its applicability and effectiveness in predicting graduates' employment data. In addition, wavelet analysis and reconstruction of graduate employment data can efficaciously improve the generalization capability of LSTM forecast model and the forecast correctness of long-term and short-term dynamic variation.Compared with other models, LSTM acquires the nonlinear dynamic characteristics of graduates' employment data and considers its nonstationary characteristics and time-dependent characteristics more comprehensively, thus showing a better forecast effect.Firstly, this paper expounds the related research work and leads to the content of this paper. Then, the basic model of deep neural network and the structure and optimization method of long short-term memory network are put forward. According to the model, taking the graduates of geological engineering as an example, the effectiveness of LSTM neural network in predicting the employment situation of college graduates is verified. Finally, the contribution and future development direction of this paper are summarized and analyzed. The main contributions of this paper are as follows:(1)
A data mining analysis model is set up by using the statistical method based on the model of cluster analysis technology to forecast the employment situation of graduates(2)
A forecast technique of graduates’ employment situation based on the long short-term memory (LSTM) recurrent neural network is proposed(3)
An LSTM forecasting model parameter optimization algorithm based on multilayer grid search is conceived to reduce the prediction error


## 2. Related Work

As one of the classic models of deep learning technology, LSTM neural network has great advantages in mining the long-term dependence of sequence data. Yang and Wang [15] provided the employment situation of graduates of civil aviation aircraft; due to its complex and irregular curve shape, it is laborious for the existing single model to realize the ideal forecast effect. To this end, Xu et al. [16] conceived a hybrid method based on SSA and SVR. The method first selects the fault feature components from the original data and then models and forecasts them separately, and obtains better experimental results than a single model. In reference to the problem that traditional methods cannot efficaciously forecast the noisy and nonlinear employment situation, Wan et al. [17] used multiscale feature fusion as the starting point and suggests and verifies the forecast technique based on the tree structure LSTM neural network. Liang et al. [18] conceived a new method for reliability forecast based on radial basis function (RBF) and genetic algorithm (GA), which realized good results by selecting appropriate parameters and network architecture. Munasypov et al. [19] used infinite impulse respond local recurrence neural network (IIR-LRNN) to forecast failures and forecast the reliability of engineering components and systems. This dynamic modeling technique is used for reliability forecast tasks for the first time. Comparing this method with radial basis function (RBF), traditional multilayer perceptron (MLP) model, and autoregressive integral moving average (ARIMA), it can be seen that the forecast correctness of this method is higher.The research on Graduates' employment prediction has positive significance for graduates' employment, enrolment, and education management. Guo and Lai [20] concluded from a psychological perspective: the psychological pressure of graduates is one of the fundamental reasons for employment difficulties. Du and Yu [21] analyzed the elements that affect graduates' employment choices and the psychological characteristics of employment. Li [22] put onward suggestions to encourage students to use time at home to fill up vacancies and strengthen psychological counseling. Kim and Cho [23] pointed out in the research that we should objectively understand the current employment situation of college students, correctly analyze the contradictions and problems of college students’ employment, make best use of the situation, respond scientifically, deepen reforms, improve policies, turn challenges into opportunities, turn pressure into motivation, and promote higher education. Huang and Yang [24] used five mathematical models such as polynomial curve fitting to perform statistical analysis on employment data from different angles. Miao [25] constructed an early warning system for college graduates' unemployment. Through the comparison of average error and time-point error, it is found that neural networks are more suitable for the forecast of the current unemployment rate of college graduates.


## 3. Materials and Methods

This paper expounds the basic composition of deep neural network and LSTM deep neural network. This paper explores the theoretical basis and feasibility of LSTM network prediction technology based on graduate employment data. Then, the basic principle of wavelet analysis is proposed, and the necessity of preprocessing graduates' employment data (i.e., wavelet analysis and reconstruction) is explained.As the reflective of typical neural network algorithm, BP neural network has better processing effect for discrete and nonlinear data because it adopts onward spread algorithm. Deep learning based on deep neural network can choose to superimpose hierarchical feature reflections of different levels according to the given task requirements, construct deep structure, and learn more nonlinear and abstract feature reflections in data. This deep neural network, which is composed of multilevel characteristics selection and superposition, is undoubtedly easier to acquire and imitate the complex composition of data. Therefore, this paper suggests using deep neural network to imitate the complicated data of graduates' employment situation, so as to realize the forecast of graduates' employment situation data. Artificial neural network (ANN) includes input layer, concealed layer, and output layer. More concealed layers can fit more complex functions, and its architecture is revealed in Figure1.Figure 1
ANN architecture.Deep neural networks further deepen the number of concealed layers of the network, using unsupervised learning to learn data features layer by layer, and the features selected from each layer are used as the data input of the next layer, that is, bottom-up learning feature selection. The output of concealed layer 1 is [26](1)h1=g1W1x+b1.Among them, the weight array is as follows:(2)W1=W111W121…W1n1W211W221…W2n1…………Wk11Wk11…Wkn1.Wij1 is the weight between the ith unit of the input layer and the jth unit of the concealed layer 1. The weight array can embody the nonlinear interactive relationship between the different influencing elements (that is, the input eigenvectors) of graduates' employment situation. The input feature vector x=x1,x2,…,xnT, the output vector h1=h11,h21,…,hk1T, b1=b11,b21,…,bn1T is the bias, n is the number of input units, and k is the number of concealed units in the concealed layer. g1 is the activation function, which realizes the nonlinear alternation of the weighted data. In deep neural networks with multilevel superposition, nonlinear activation functions can enable a more expressive model to learn more abstract feature reflections.Based on the feature reflection learned by concealed layer 1, the output of concealed layer 2 is(3)h2=g2W2h1+b2.By analogy, the last output layer is(4)y^x=gyWyhH+by.Given the deep neural network structure and activation function, the objective of learning is to fine-tune supervised learning based on top-down to find the best parameterW^=W^1,W^2,…,W^y. b^=b^1,b^2,…,b^y minimizes the experience risk, namely,(5)min1N∑i=1NLyi,y^ixi.Among them, the loss function adopts the form of absolute value error:(6)Lyi,y^ixi=yi−y^ixi.In order to prevent overfitting, it is requisite to further put forward a standardization term that reflects the complicacy of the network structure, namely,(7)min1N∑i=1NLyi,y^ixi+λJθ.Among them,Jθ embodies the complicacy of the network structure; the more complex the network structure θ, the larger J(θ). After the model is established with the training set, it can be tested on the test set data first, and the prediction accuracy of this model on the test set is a good guiding figure. If the accuracy rate is greater than 90%, it shows that the model established on the training set is feasible. This model can be used to predict other data, and the percentage of the prediction results is correct.LSTM neural network is a distinct cyclic neural network (RNN) which can dispose of the long-term dependence of employment data. LSTM neural network structure (Figure2) includes subnetworks (storage modules) connected circularly.Figure 2
LSTM execution steps.Noise data should be removed as much as possible in the data preparation stage. By analyzing the sample data of graduates, the information entropy of sample space and the expected information entropy of test attributes are calculated to obtain the information gain ratio of test attributes, and the decision tree is constructed. Therefore, different classifications about samples are formed, and each sample data is classified and judged, so that the employment situation of sample data can be accurately predicted. Batch-Normalization (BN) can efficaciously solve the matter of gradient disappearance, and Dropout technology can relieve the matter of overfitting by preventing neuronal coadaptation. Therefore, when designing the LSTM neural network, this article distinctly added the BN layer and the Dropout layer to optimize the neural network structure. The composition of the depth LSTM neural network calculation diagram is shown in Figure3.Figure 3
The composition of the deep LSTM neural network calculation graph.Use the forget gateft to determine the information that needs to be removed from the cell:(8)ft=σbf+Wfxt+Ufht−1.Among them,σ is the sigmoid activation function, the data flow weight is set to a value between 0 and 1, 0 means that the data is completely deleted, and 1 means that all the data is retained. xt is the real-time output vector, ht is the current concealed layer vector, and bf, Wf, and Uf are the cyclic weights of the bias, input weight, and forget gate, respectively.Second, update the state of data in the cell. Supposegt is an exterior input gate between 0 and 1 controlled by the sigmoid activation function:(9)gt=σbg+Wgxt+Ught−1.Then, the cell stateCt updated on the basis of Ct−1 is(10)Ct=ftCt−1+gttanhbc+Wcxt+Ucht−1.Lastly, the information output controlled by output gateot is(11)ht=ottanhCt.Among them, the output gate is(12)ot=σbo+Woxt+Uoht−1.


## 4. Result Analysis and Discussion

### 4.1. Data Description

In deep learning, sample data are usually divided into training set, verification set, and test set, as shown in Table1. When training deep neural networks, the feature standardization of sample data plays an important role in giving full play to the best effect of deep learning algorithm. For employment situation x1,x2,…,xt, its standardized treatment is(13)yi=xi−x¯s,i=1,2,…,t.Table 1
Description of sample data division.
DataGraduation daysTimeTraining set10002015/01/29–2019/06/21Authentication set1202019/06/22–2020/01/05Test set1202020/01/06–2020/07/31Among them,x¯=∑xi/t,s=1/t−1∑xi−x¯2.In this paper, the optimization algorithm of LSTM deep neural network training adopts the RMSprop algorithm. In order to prevent the overfitting phenomenon of model training, on the basis of introducing penalty items, the Dropout method is further adopted in the training process; that is, some concealed units in the concealed layer are randomly removed.The observed sample data of college graduates' employment situation is divided into a training subset and a test subset. In the training subset, an intermediate candidate prediction model is generated through internal specifications, and in the test subset, an intermediate candidate prediction model for employment is collected through external specifications. In the process of modeling, the self-organizing neural network algorithm screens the input variables of college graduates' employment situation at all levels by adopting relevant specifications and then combines them to obtain the screening model of the next level, until the best employment complexity model is finally obtained.Divide the sample data setW of university graduates' employment situation into training set A and test set B; then W=A∪B. If you shape the prediction model, you need to divide the prediction subset C again to ensure that W=A∪B∪C.To shape the general functional relationship between the outputy of the prediction model and the input x1,x2,…,xn, the Kolmogorov–Gabor polynomial is as follows [27]:(14)y=fx1,x2=a0+a1x1+a2x2+a3x12+a4x22+a5x1x2.And treat each of the monomials asm input models in the original structure of the modeling network:(15)v1=a0,v2=a1x1,v3=a2x2,…,v6=a5x1x2.Self-organizing process adaptively forms the first-level intermediate model:(16)zk=fkvi,vj,i,=1,2,…,6.And in the training setA, the parameter prediction method is used to predict the coefficient of zk. In the test set B, the competition model zk is filtered through external specifications, and the intermediate candidate prediction model wk=zk is collected and regarded as the input of the second layer of the network.


### 4.2. Result Analysis

From the perspective of the whole employment area, most graduates choose the eastern, southern, or coastal areas with high wages and good environment for employment, while relatively few graduates go to the western areas for exercise, entrepreneurship, and development. From the perspective of employment units, there are many large- and medium-sized state-owned enterprises and institutions, and most construction units have Grade A and Grade B qualifications; that is, the selection rate of units with good development prospects is high. In the past, graduates could only choose key university graduates for development, but there were fewer graduates from local and county-level small- and medium-sized enterprises. Taking geological engineering graduates as sample set, based on the data reconstructed by wavelet and original data, the LSTM neural network model for predicting graduates' employment status is established to explore the prediction effect of LSTM neural network and the feasibility of reconstructing graduates’ employment status data by wavelet in empirical analysis. The forecast results are shown in Figures4–6, respectively. The LSTM neural network has a good predictive ability on the actual employment data of graduates.Figure 4
(a) The forecast effect of the training set (wavelet reconstruction data). (b) The forecast effect of the training set (original data).
(a)(b)Figure 5
(a) Validation set forecast effect (wavelet reconstruction data). (b) Validation set forecast effect (original data).
(a)(b)Figure 6
(a) The forecast effect of the test set (wavelet reconstruction data). (b) Test set forecast effect (original data).
(a)(b)Figures4–6 show the point-to-point static prediction effect. LSTM neural network based on wavelet reconstruction data can better predict the long-term dynamic trend of financial time series data. Figure 7 shows the short-term dynamic forecast effect of the employment situation of college graduates. Figure 8 further shows the forecast effect of LSTM neural network on the long-term dynamic change trend of graduates' employment situation, and its forecast effect is basically consistent with the real value.Figure 7
(a) Short-term dynamic forecast effect (training set). (b) Short-term dynamic forecast effect (validation set). (c) Short-term dynamic forecast effect (test set).
(a)(b)(c)Figure 8
(a) Long-term dynamic forecast effect (training set). (b) Long-term dynamic forecast effect (validation set). (c) Long-term dynamic forecast effect (test set).
(a)(b)(c)The relevance of employment situation is weakened by a large amount of noise interference, and some weak association rules almost dissipate, resulting in the sparse distribution of relevance samples and the majority of noise samples. This unbalanced sample distribution brings great obstacles to the training process of forecast model [28–33]. In order to compare the forecastive capability of LSTM deep neural networks, three models of MLP, support vector machine (SVM), and generalized autoregressive conditional heteroscedasticity (GARCH) are selected to forecast the employment situation of graduates. Among them, the optimal parameter adjustment of other machine learning algorithms is determined based on grid search, and the measurement model is the GARCH (1, 1) model. The Theil unequal coefficient (T) and the average absolute error (M) are used to measure the forecast correctness of the model. The prediction results are shown in Table 2. Among them,(17)T=1/T∑t=1TYtpre−Yttrue21/T∑t=1TYtpre2+1/T∑t=1TYttrue2.Table 2
Comparison of forecast effects.
Training setAuthentication setTest setTMTMTMLSTM0.00070.00150.00050.00080.00060.0009MLP0.00610.00310.00820.01390.01470.0278SVM0.00630.01120.00430.00780.00980.0193GARCH0.00790.0099——0.04030.0622The value is within [0, 1],T = 0 means that the forecasted value is completely fitted to the true value, and the forecastive capability is the strongest, and T = 1 means the forecastive capability is the worst:(18)M=1T∑t=1TYtpre−YttrueYttrue.Based on the forecast results in Table2, it can be seen that LSTM neural network has the best forecast capability in test set, which can efficaciously balance the forecast effect of data in training set, verification set, and test set. The possible reasons are that the shallow machine learning algorithm is easy to fall into the overfitting dilemma, and its capability to learn and select the features of input feature vectors is weak, and the time-dependent features of graduates' employment data cannot be acquired.However, GARCH model, a traditional econometric method, has not realized good forecast results in both training set and test set. The possible reasons are as follows: firstly, GARCH model is a linear model, which cannot acquire the nonlinear dynamic relationship among economic variables and then cannot describe the nonlinear characteristics of graduates' employment data well. The establishment of GARCH and other employment status models requires compressible data. It is usually necessary to perform differential or logarithmic differential processing on the data, which will undoubtedly cause the loss of employment data information. Both MLP and SVM, which are shallow machine learning algorithms, and LSTM neural networks, which are deep learning, can efficaciously dispose of the nonstationary information of the original data signal and embody the nonlinear interaction between variables, thus describing the nonlinear dynamic characteristics of data. LSTM neural network not only overcomes the dependence on index selection in the modeling process of traditional econometric model, but also overcomes the defect that linear econometric model cannot embody the nonlinear interaction of variables. At the same time, it overcomes the shortcomings that shallow machine learning algorithm cannot describe the correlation of employment situation, and the data feature learning capability is weak, and it is easy to overfit. Therefore, compared with other models, LSTM acquires the nonlinear dynamic characteristics of graduates' employment data and considers its nonstationary characteristics and time-dependent characteristics more comprehensively, thus showing a better forecast effect.


### 4.3. Discussion

This paper explores the theoretical basis and practical application of LSTM neural network in the prediction of graduates' employment situation. Wavelet analysis is used to decompose and reconstruct graduates' employment situation, so as to eliminate the influence of short-term random disturbance noise, and improve the prediction accuracy of the model for out-of-sample data and the prediction ability for future trends. Secondly, compared with machine learning algorithms such as multilayer perceptron and support vector machine, and metrology models such as GARCH, LSTM neural network has higher prediction accuracy. This also fully proves that LSTM neural network has a strong ability to predict the actual employment situation of graduates. Undeniably, although deep learning has been widely used and achieved great success in many artificial intelligence fields such as speech recognition and automatic driving, its application research in the field of employment prediction is still relatively few. In this paper, the feasibility of deep learning LSTM neural network for predicting graduates' employment situation is explored theoretically, and the empirical results based on employment prediction data prove the applicability and effectiveness of LSTM neural network in the research field of graduates' employment situation prediction. This can be used for reference in building a prediction model of graduates' employment situation under the background of big data and artificial intelligence. Complex representation is expressed by simple representation through hierarchical feature learning, which solves the core problem in representation learning and overcomes the defect of dependence on feature vector selection in existing employment prediction research methods. At the same time, deep learning has great advantages in the processing of unstructured data such as text and pictures, which is of great significance for mining employment forecast information. Therefore, it is still worthy of further exploration to combine deep learning algorithms with other employment forecasting research fields to expand the existing employment forecasting research methods. This paper focuses on LSTM neural network to predict the future trends of graduates' employment, but how to build a graduate employment prediction model based on deep learning algorithm to improve the prediction accuracy of short-term random disturbance noise still needs further exploration.With the rapid development of China's economy and the continuous improvement of the economic situation, as well as the government's increasing efforts to regulate and control the employment policies of graduates, the employment positions of newly added labor force are constantly increasing, and the demand for graduates will continue to increase. However, influenced by the overall employment situation in China, the absolute number of graduates has increased year by year, but the growth rate of social demand for graduates is not synchronized, and the contradiction between supply and demand still exists. Therefore, the employment situation of graduates is still grim. Graduates should abandon the traditional values and industry concepts and examine and choose their recognized occupations with a positive and pragmatic spirit and a new and broad vision.


## 4.1. Data Description

In deep learning, sample data are usually divided into training set, verification set, and test set, as shown in Table1. When training deep neural networks, the feature standardization of sample data plays an important role in giving full play to the best effect of deep learning algorithm. For employment situation x1,x2,…,xt, its standardized treatment is(13)yi=xi−x¯s,i=1,2,…,t.Table 1
Description of sample data division.
DataGraduation daysTimeTraining set10002015/01/29–2019/06/21Authentication set1202019/06/22–2020/01/05Test set1202020/01/06–2020/07/31Among them,x¯=∑xi/t,s=1/t−1∑xi−x¯2.In this paper, the optimization algorithm of LSTM deep neural network training adopts the RMSprop algorithm. In order to prevent the overfitting phenomenon of model training, on the basis of introducing penalty items, the Dropout method is further adopted in the training process; that is, some concealed units in the concealed layer are randomly removed.The observed sample data of college graduates' employment situation is divided into a training subset and a test subset. In the training subset, an intermediate candidate prediction model is generated through internal specifications, and in the test subset, an intermediate candidate prediction model for employment is collected through external specifications. In the process of modeling, the self-organizing neural network algorithm screens the input variables of college graduates' employment situation at all levels by adopting relevant specifications and then combines them to obtain the screening model of the next level, until the best employment complexity model is finally obtained.Divide the sample data setW of university graduates' employment situation into training set A and test set B; then W=A∪B. If you shape the prediction model, you need to divide the prediction subset C again to ensure that W=A∪B∪C.To shape the general functional relationship between the outputy of the prediction model and the input x1,x2,…,xn, the Kolmogorov–Gabor polynomial is as follows [27]:(14)y=fx1,x2=a0+a1x1+a2x2+a3x12+a4x22+a5x1x2.And treat each of the monomials asm input models in the original structure of the modeling network:(15)v1=a0,v2=a1x1,v3=a2x2,…,v6=a5x1x2.Self-organizing process adaptively forms the first-level intermediate model:(16)zk=fkvi,vj,i,=1,2,…,6.And in the training setA, the parameter prediction method is used to predict the coefficient of zk. In the test set B, the competition model zk is filtered through external specifications, and the intermediate candidate prediction model wk=zk is collected and regarded as the input of the second layer of the network.


## 4.2. Result Analysis

From the perspective of the whole employment area, most graduates choose the eastern, southern, or coastal areas with high wages and good environment for employment, while relatively few graduates go to the western areas for exercise, entrepreneurship, and development. From the perspective of employment units, there are many large- and medium-sized state-owned enterprises and institutions, and most construction units have Grade A and Grade B qualifications; that is, the selection rate of units with good development prospects is high. In the past, graduates could only choose key university graduates for development, but there were fewer graduates from local and county-level small- and medium-sized enterprises. Taking geological engineering graduates as sample set, based on the data reconstructed by wavelet and original data, the LSTM neural network model for predicting graduates' employment status is established to explore the prediction effect of LSTM neural network and the feasibility of reconstructing graduates’ employment status data by wavelet in empirical analysis. The forecast results are shown in Figures4–6, respectively. The LSTM neural network has a good predictive ability on the actual employment data of graduates.Figure 4
(a) The forecast effect of the training set (wavelet reconstruction data). (b) The forecast effect of the training set (original data).
(a)(b)Figure 5
(a) Validation set forecast effect (wavelet reconstruction data). (b) Validation set forecast effect (original data).
(a)(b)Figure 6
(a) The forecast effect of the test set (wavelet reconstruction data). (b) Test set forecast effect (original data).
(a)(b)Figures4–6 show the point-to-point static prediction effect. LSTM neural network based on wavelet reconstruction data can better predict the long-term dynamic trend of financial time series data. Figure 7 shows the short-term dynamic forecast effect of the employment situation of college graduates. Figure 8 further shows the forecast effect of LSTM neural network on the long-term dynamic change trend of graduates' employment situation, and its forecast effect is basically consistent with the real value.Figure 7
(a) Short-term dynamic forecast effect (training set). (b) Short-term dynamic forecast effect (validation set). (c) Short-term dynamic forecast effect (test set).
(a)(b)(c)Figure 8
(a) Long-term dynamic forecast effect (training set). (b) Long-term dynamic forecast effect (validation set). (c) Long-term dynamic forecast effect (test set).
(a)(b)(c)The relevance of employment situation is weakened by a large amount of noise interference, and some weak association rules almost dissipate, resulting in the sparse distribution of relevance samples and the majority of noise samples. This unbalanced sample distribution brings great obstacles to the training process of forecast model [28–33]. In order to compare the forecastive capability of LSTM deep neural networks, three models of MLP, support vector machine (SVM), and generalized autoregressive conditional heteroscedasticity (GARCH) are selected to forecast the employment situation of graduates. Among them, the optimal parameter adjustment of other machine learning algorithms is determined based on grid search, and the measurement model is the GARCH (1, 1) model. The Theil unequal coefficient (T) and the average absolute error (M) are used to measure the forecast correctness of the model. The prediction results are shown in Table 2. Among them,(17)T=1/T∑t=1TYtpre−Yttrue21/T∑t=1TYtpre2+1/T∑t=1TYttrue2.Table 2
Comparison of forecast effects.
Training setAuthentication setTest setTMTMTMLSTM0.00070.00150.00050.00080.00060.0009MLP0.00610.00310.00820.01390.01470.0278SVM0.00630.01120.00430.00780.00980.0193GARCH0.00790.0099——0.04030.0622The value is within [0, 1],T = 0 means that the forecasted value is completely fitted to the true value, and the forecastive capability is the strongest, and T = 1 means the forecastive capability is the worst:(18)M=1T∑t=1TYtpre−YttrueYttrue.Based on the forecast results in Table2, it can be seen that LSTM neural network has the best forecast capability in test set, which can efficaciously balance the forecast effect of data in training set, verification set, and test set. The possible reasons are that the shallow machine learning algorithm is easy to fall into the overfitting dilemma, and its capability to learn and select the features of input feature vectors is weak, and the time-dependent features of graduates' employment data cannot be acquired.However, GARCH model, a traditional econometric method, has not realized good forecast results in both training set and test set. The possible reasons are as follows: firstly, GARCH model is a linear model, which cannot acquire the nonlinear dynamic relationship among economic variables and then cannot describe the nonlinear characteristics of graduates' employment data well. The establishment of GARCH and other employment status models requires compressible data. It is usually necessary to perform differential or logarithmic differential processing on the data, which will undoubtedly cause the loss of employment data information. Both MLP and SVM, which are shallow machine learning algorithms, and LSTM neural networks, which are deep learning, can efficaciously dispose of the nonstationary information of the original data signal and embody the nonlinear interaction between variables, thus describing the nonlinear dynamic characteristics of data. LSTM neural network not only overcomes the dependence on index selection in the modeling process of traditional econometric model, but also overcomes the defect that linear econometric model cannot embody the nonlinear interaction of variables. At the same time, it overcomes the shortcomings that shallow machine learning algorithm cannot describe the correlation of employment situation, and the data feature learning capability is weak, and it is easy to overfit. Therefore, compared with other models, LSTM acquires the nonlinear dynamic characteristics of graduates' employment data and considers its nonstationary characteristics and time-dependent characteristics more comprehensively, thus showing a better forecast effect.


## 4.3. Discussion

This paper explores the theoretical basis and practical application of LSTM neural network in the prediction of graduates' employment situation. Wavelet analysis is used to decompose and reconstruct graduates' employment situation, so as to eliminate the influence of short-term random disturbance noise, and improve the prediction accuracy of the model for out-of-sample data and the prediction ability for future trends. Secondly, compared with machine learning algorithms such as multilayer perceptron and support vector machine, and metrology models such as GARCH, LSTM neural network has higher prediction accuracy. This also fully proves that LSTM neural network has a strong ability to predict the actual employment situation of graduates. Undeniably, although deep learning has been widely used and achieved great success in many artificial intelligence fields such as speech recognition and automatic driving, its application research in the field of employment prediction is still relatively few. In this paper, the feasibility of deep learning LSTM neural network for predicting graduates' employment situation is explored theoretically, and the empirical results based on employment prediction data prove the applicability and effectiveness of LSTM neural network in the research field of graduates' employment situation prediction. This can be used for reference in building a prediction model of graduates' employment situation under the background of big data and artificial intelligence. Complex representation is expressed by simple representation through hierarchical feature learning, which solves the core problem in representation learning and overcomes the defect of dependence on feature vector selection in existing employment prediction research methods. At the same time, deep learning has great advantages in the processing of unstructured data such as text and pictures, which is of great significance for mining employment forecast information. Therefore, it is still worthy of further exploration to combine deep learning algorithms with other employment forecasting research fields to expand the existing employment forecasting research methods. This paper focuses on LSTM neural network to predict the future trends of graduates' employment, but how to build a graduate employment prediction model based on deep learning algorithm to improve the prediction accuracy of short-term random disturbance noise still needs further exploration.With the rapid development of China's economy and the continuous improvement of the economic situation, as well as the government's increasing efforts to regulate and control the employment policies of graduates, the employment positions of newly added labor force are constantly increasing, and the demand for graduates will continue to increase. However, influenced by the overall employment situation in China, the absolute number of graduates has increased year by year, but the growth rate of social demand for graduates is not synchronized, and the contradiction between supply and demand still exists. Therefore, the employment situation of graduates is still grim. Graduates should abandon the traditional values and industry concepts and examine and choose their recognized occupations with a positive and pragmatic spirit and a new and broad vision.


## 5. Conclusions

From the forecast results, we can see that the employment rate of college graduates in China shows a stable trend, and the growth and decline trends are very small. Due to the large population base and rapid growth in China, the total population and the number of college graduates will show an increasing trend in the next few years. The test data are added to the classification tree, and the difference between the employment rate and the actual employment rate displayed by the classification tree is used as the standard to judge the correctness. This paper puts onward a system-level employment forecast technique based on LSTM recurrent neural network, including training, forecast, and parameter optimization of LSTM model. In this paper, the theoretical basis and practical application of LSTM neural network applied to the forecast of graduates' employment situation data are searching, and wavelet analysis is conceived to decompose and reconstruct graduates' employment situation data, so as to eliminate the influence of short-term random disturbance noise, and improve the forecast correctness of the model to the data outside the sample and the forecast capability of future trends. LSTM neural network not only overcomes the dependence on index selection in the modeling process of traditional econometric model, but also overcomes the defect that linear econometric model cannot embody the nonlinear interaction of variables. At the same time, it overcomes the shortcomings that shallow machine learning algorithm cannot describe the correlation of employment situation, and the data feature learning capability is weak, and it is easy to overfit. Therefore, compared with other models, LSTM acquires the nonlinear dynamic characteristics of graduates' employment data, and considers its nonstationary characteristics and time-dependent characteristics more comprehensively, thus showing a better forecast effect.LSTM neural network has higher prediction accuracy. Schools should identify the entry point of self-development and alumni career, actively carry out exchanges and cooperation in various fields such as production, education, and research, recommend outstanding graduates to work in enterprises and institutions where alumni are located, build a good platform for graduates' employment, and continuously improve the quality of graduates' employment. For those students who were not employed in those years, we should make great efforts to track them, classify them, set up their graduates' information data bank, register their employment intentions, constantly improve the supervisory system of unemployed graduates, track their whereabouts at any time, and help them find jobs smoothly. At the same time, it is requisite to strengthen the contact with alumni and previous graduates, collect information in various ways, and provide various employment information for the latecomers.



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*Source: 5787355-2021-09-27.xml*