Construction and Optimization of Accurate Pushing System for English Literature Reading Resources Driven by Big Data Technology

Abstract

This study constructs an accurate push system for English literature reading resources based on big data technology, which realizes the personalized and accurate push of English literature reading resources. The system adopts a layered architecture design driven by big data, and realizes data integration and analysis by extensively collecting multi-source data and applying data mining technology, machine learning technology and data analysis technology. Meanwhile, by constructing user profiles, resource features are extracted and based on collaborative filtering and content recommendation algorithms to ensure the effectiveness of the system. Two classes with comparable levels were selected for experimental validation, with the experimental group using the accurate push system to read and the control group reading in the traditional way. The results show that students in the experimental group have significant improvement in reading efficiency, reading comprehension accuracy and independent reading time, and the satisfaction also indicates that the system can meet the students' individualized reading needs and enhance their reading interest, which provides a strong support for the construction of the precision push system for English literature reading resources.