Lack of concentration is an attention disorder that is common among teenagers, and it directly affects people’s learning and work efficiency. Most of the traditional attention detection methods rely on the observation of expressions, postures, and other behaviors and fail to objectively and accurately reflect attention states. Amid the rapid development of physiological detection technology, attention detection based on electroencephalography (EEG) signals has received considerable attention recently. However, related studies still have the problem of low detection accuracy. In this study, the EEG signals of 155 college students in the three states of being focused, distracted, and relaxed are collected, and the three attention states are identified by various machine learning methods on the basis of the wavelet features, differential entropy features and power spectrum features of the signals. The results show that these features of EEG signals can effectively distinguish the attention states of the subjects. The average accuracy of the detection method based on symmetrical dual-channel features is (80.84±3)%, and the detection precision of this method is significantly higher than that of the method based on single-channel features.