AIGC German Vocabulary Memory Optimization Algorithm Based on Machine Learning
Bo Wang · 2025
Traditional vocabulary learning methods often lack individuality and interest, and it is difficult to meet the diverse needs of learners. Therefore, this study proposes an innovative algorithm framework, which combines ML algorithm and AIGC (Artificial Intelligence Generated Content) technology, and can intelligently recommend and generate individualized vocabulary learning content according to learners' individual characteristics and learning progress. In terms of methods, this article constructs an algorithm framework including four core parts: data input, analysis and processing, model decision-making and content output, and realizes the individualized function of the algorithm through key steps such as feature selection, data preprocessing, model construction and training, and AIGC content generation mechanism. The experimental design adopts the comparative research method to compare the learners who use the traditional vocabulary memory method with those who use the algorithm to verify the effectiveness of the algorithm. The experimental results show that the learners who use the algorithm have obvious advantages in vocabulary memory efficiency, learning interest and long-term memory. The conclusion of this article provides new ideas and methods for German vocabulary teaching, which has important practical significance and application prospects.