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.
Highlights
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]
In order to compare the forecastive capability of long short-term memory (LSTM) deep neural networks, three models of multilayer perceptron (MLP), support vector machine (SVM), and generalized autoregressive conditional heteroscedasticity (GARCH) are selected to forecast the employment situation of graduates
LSTM neural network overcomes the dependence on index selection in the modeling process of traditional econometric model, and overcomes the defect that linear econometric model cannot embody the nonlinear interaction of variables
Summary
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]. 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. (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
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