| Summary: | This paper utilizes data mining algorithms to predict the evaluation value of new items by target users in an interactive learning environment. To enhance data quality, redundant data in the dataset is eliminated. To provide better learning recommendations and personalized services, prediction accuracy is assessed by calculating the deviation between predicted and actual ratings. Teachers and students can use the analysis results of feature weights, correct word cut scores, and other indicators obtained from data mining as key learning references. In the data mining analysis of students’ English learning data, the feature weight is 0.921, which helps to assess students’ knowledge mastery and learning effect more accurately.
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