The invention discloses an intrusion detection method based on semi-supervised learning. The method comprises the steps of selecting an initial mixed sample set with samples with labels and unlabeled samples to be tested, calculating information gain of each characteristic value in a characteristic space, and completing characteristic selection based on information entropy; then, screening the samples with the labels based on the characteristic selection of the information entropy, using new screened training data for semi-supervised training of a classifier based on LapSVM, and utilizing the classifier after training is finished to classify the unlabeled samples to be tested; according to a detection index, determining the best evaluation value of the detection index, and outputting a classification result corresponding to the best evaluation value of the detection index. According to the intrusion detection method based on semi-supervised learning, the characteristic selection method is adopted to deal with redundancy phenomena easily occurring in network environment data, a semi-supervised learning model is established by utilizing a small number of samples with labels and a large amount of unlabeled data, the false alarm rate is reduced, and the detection rate is increased; meanwhile, the data redundancy can be reduced, and the detection efficiency is improved.