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

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.