Abstract: An entity transaction attribute determination method for determining an attribute state or an attribute category of a to-be-predicted entity on a preset transaction is provided. The method comprises obtaining a plurality of historical relational networks sequentially arranged under a temporal order; determining, for each of the historical relational networks and through vector fusion of neighbor nodes, a plurality of description vectors of the to-be-predicted entity; processing, through a pre-trained time-series neural network, the description vectors to obtain an output result; and determining, according to the output result, the attribute state or the attribute category of the preset transaction attribute for the to-be-predicted entity. The method improves the accuracy of predicting a preset transaction attribute of an entity through the analysis of the description vectors.

Abstract: An entity transaction attribute determination method for determining an attribute state or an attribute category of a to-be-predicted entity on a preset transaction is provided. The method comprises obtaining a plurality of historical relational networks sequentially arranged under a temporal order; determining, for each of the historical relational networks and through vector fusion of neighbor nodes, a plurality of description vectors of the to-be-predicted entity; processing, through a pre-trained time-series neural network, the description vectors to obtain an output result; and determining, according to the output result, the attribute state or the attribute category of the preset transaction attribute for the to-be-predicted entity. The method improves the accuracy of predicting a preset transaction attribute of an entity through the analysis of the description vectors.