<p>Most of the existing anomaly data detection models are implemented by supervised or unsupervised algorithms. However, these algorithms have a strong dependence on whether the samples are fully labeled or not. Semi-supervised learning is an effective approach for detecting anomalous data. In anomalous data detection models based on semi-supervised learning, unlabeled data are usually assumed to be normal data. However, when the unlabeled dataset contains a small amount of abnormal data, the performance of a semi-supervised learning abnormal data detection model can be greatly degraded. A co-training semi-supervised anomaly data detection model based on random forest is introduced to overcome degradation and maintain stable detection performance. The claimed random forest based co-training anomaly data detection has three stages: (1) In the data preprocessing stage, the data are divided into two sub-view datasets using a feature importance ranking algorithm based on extremely randomized trees; (2) In the co-training stage, it labels the unlabeled samples with pseudo-labels using two random forest classifiers until all the two sub-view data are labeled; (3) In the third stage, to further improve the accuracy of pseudo labeling, a soft voting mechanism is used to synthesize a final classifier model and improve the generalization ability of the classifier. The proposed random forest-based co-training mechanism, leveraging iterative pseudo-labeling with dual classifiers and soft voting, effectively addresses performance degradation caused by limited anomalies in unlabeled data, achieving superior F1 and AUC scores over six state-of-the-art models across 11 benchmark datasets.</p>