Abstract
Abstract In this paper, we first obtain the most representative subset of learning behavior features from big data and evaluate each feature value using a genetic algorithm to obtain its weight parameter. Then a similar least squares method is used to establish behavioral performance indicators through temporality, and the loss function is used to classify the indicators to obtain accurate teaching parameters. Finally, the K-means algorithm is used to mine the spacing distance of different log data and obtain the corresponding temporal feature vector to the learning behavior data with similarity. The application effectiveness of this teaching platform was tested, and the results showed that the propagation time of the big data platform takes only 112ms, the start-up time is 158ms, and the highest access number ratio can reach 0.82. The passing rate of students using this platform is as high as 73% on average, and the average length of independent learning reaches 6.5 hours. It shows that the online German teaching platform built on the basis of big-number technology has cultivated students’ international vision and cross-cultural communication skills.
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