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There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and in real-time evaluate cognitive load of user based the data. The study explores the possibility of using the features extracted from high-frequency interaction events to evaluate cognitive load to respond to the challenge. Specifically, back-propagation neural networks, along with two feature selection methods nBset and SFS, were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: 1 demonstrating the use of combining machine learning techniques and the HFI features in automatically evaluating cognitive load; 2 showing the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.