Based on the RI-GRU Helped English Reading Interested in Classification Prediction
With the rapid growth of information generated in various application scenarios,the storage of English reading materials is also increasing.How to fully tap the potential data information has become a research difficulty.The prediction of English reading interest aims to explore the interest of English learners.With the GRU structure of the gated cycle unit as the core,a prediction model of English reading interest(RI-GRU)was built.Taking the proportion data of English reading materials as an example,by capturing the characteristics of various relationship attributes,the RNN network realized by GRU was used to calculate various errors under dif-ferent sequence data,and the optimal model parameters were found with the goal of minimizing errors,to achieve a high accuracy of English reading interest prediction.The final experiment shows that,compared with other models,the RI-GRU prediction model can effectively characterize the characteristics of English reading data,re-duce the classification errors,and the classification prediction effect is better.
English readinginterest predictionGRURNN networkrelationship properties
万方数据
维普
