Sampling method for unbalanced transaction data of fictitious assets

The invention discloses a sampling method for unbalanced transaction data of fictitious assets. The method includes the following steps that abnormal transaction data in fictitious asset transaction are defined as a minority class, and oversampling is carried out on samples of the minority class by means of an improved SMOTE method in order to increase the number of the samples of the minority class; normal transaction data in fictitious asset transaction are defined as a majority class, and undersampling is conducted on samples of the majority class by means of a distance-based DUS method in order to decrease the number of the samples of the majority class; a scaling factor is set to adjust the proportion of the oversampling number and the undersampling number. The sampling method for unbalanced transaction data is applied to abnormal transaction detection of the fictitious assets, the calculated amount of abnormal transaction detection can be greatly reduced, and a high accuracy rate can be reached.