Aiming at the problem of fast and accurate detection of bad information under complex environment background, a humanmachine collaboration system for bad information detection based on rapid serial visual presentation (RSVP) is proposed. Firstly, using the fast-wearing portable acquisition system collected the EEG data of 12 subjects; then the Mallat algorithm was used to extract the lower-dimensional time-frequency features of the EEG data, and EEG signal classification uses artificial neural network (ANN) and support vector machine (SVM). Finally, different times of superimposed average data are introduced in the training set to improve the classification performance of the model. The experimental results show that at least 2 targets are correctly output on average in 60 images containing 3 targets, and the AUC value reaches 0. 9. The system has good performance in the detection of small batch data sets and bad image information with complex environmental changes, and has improved efficiency compared with manual detection.