Disclosed is a behavior prediction method, belonging to the technical field of information. A behavior of a user is predicted by using data; however, attributes of the existing data are completely different, and actually, these pieces of data possibly have no association; therefore, a one-size-fits-all data processing method is not suitable for precise prediction under big data at present. The method comprises: step 1, fusing One-Hot Encoding and Label Encoding into a multi-dimensional feature code; step 2, representing collected sample data as the multi-dimensional feature code in step 1; step 3, enriching existing label data by using a generative adversarial network; step 4, integrating a plurality of models, carrying out repeated training to generate a weight factor of each model, and then classifying the data obtained in step 3 after an integrated model with weights is obtained; and step 5, outputting a predicted behavior. By means of the method, data classification is more accurate, and a behavior of a user is effectively predicted.
BEHAVIOR PREDICTION METHOD