Prediction of regional primary school enrollment scale based on attention mechanism
CHEN Yu1,2， XING Rui3， LEI Jianjun1
(1.School of Computer， Hubei University of Education， Wuhan 430205，China；2.Hubei Education Cloud Service Engineering Technology Research Center， Wuhan 430205，China；3.Hubei Education Information Development Center， Wuhan 430071，China)
Abstract：The school-age population is an important basis for the allocation of educational resources in a region. An accurate prediction of the enrollment scale of the primary schools for the next year in the region can provide auxiliary decision support for the allocation of educational resources by the education and management departments in the region. In order to predict the enrollment scale of primary schools in a region， a circular network prediction model is proposed based on the attention mechanism which considered the correlation between regional economy， population and the enrollment scale of primary schools. This model is based on the Long Short-time Memory network model， and introduces the attention mechanism to automatically extract the correlation the primary school enrollment scale and the characteristics like economy and population， and further enhance the information expression of critical historical moments to improve the prediction accuracy. Using real data sets，the test results show thatthis model has improved in many evaluation indexes， and has a more accurate and stable prediction effect compared with other models.
2021, Vol. 55 
