Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 261-266.doi: 10.11896/jsjkx.220300120

• Big Data & Data Science • Previous Articles     Next Articles

Study on Prediction of Educational Statistical Data Based on DE-LSTM Model

LIU Bao-bao, YANG Jing-jing, TAO Lu, WANG He-ying   

  1. School of Computer Science,Xi'an Engineering University,Xi'an 710000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:LIU Bao-bao,born in 1982,Ph.D,master supervisor,is a senior member of China Computer Federation.His main research interests include machine learning,deep learning,intelligent image processing and interpretation,education statistics and big data analysis.
  • Supported by:
    Special Scientific Research Plan for Information Security of Shaanxi Provincial Department of Education(20JX004) and Natural Science Research Plan in Shaanxi Province of China(General Program)(2020JM-574).

Abstract: At present,educational data presents the characteristics of large amount of data and diverse data types.Accurate and effective analysis and prediction of educational statistical data,which has important reference value for the formulation of relevant policies in education sector and social development.In this paper,DE-LSTM model is proposed,which takes the annual enrollment of a city as the data basis.The proposed model uses differential evolution algorithm to optimize the hidden layer nodes and lear-ning rate in the long-term and short-term memory neural network and has the better prediction performance in compared with the LSTM and BP models.Furthermore,effectiveness of the proposed DE-LSTM model is verified by a large number of simulation experiments.

Key words: BP neural network, Differential evolution algorithm, Education Statistics, Long and short term memory network, Time series prediction

CLC Number: 

  • TP183
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