Abstract
There are many problems in abnormal detection of online English learning behaviour, such as large error and high detection time. Therefore, a detection method based on feature extraction is proposed. Firstly, frequent pattern mining method is used to collect learners' behaviour data, and the data is collected and preprocessed. Then, the classification constraints are set by support vector machine to complete the data classification. Finally, the sequence minimum eigenvalue method is used to train the abnormal data, extract the high frequency features of the abnormal data, establish the anomaly detection model, and realise the anomaly detection. Experimental results show that the highest detection error of this method is 1.2%, and the highest time cost is 1.8 s. Therefore, this method can effectively reduce the detection error and time cost, and is feasible.
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