Microstructures of Cu-4.7Sn (％) alloys prepared by two-phase zone continuous casting (TZCC) technology contain large columnar grains and small grains.A compound grain structure,composed of a large columnar grain and at least one small grain within it,is observed and called as grain-covered grains (GCGs).Distribution of small grains,their numbers and sizes as well as numbers and sizes of columnar grains were characterized quantitatively by metallographic microscope.Back propagation (BP) artificial neural network was employed to build a model to predict microstructures produced by different processing parameters.Inputs of the model are five processing parameters,which are temperatures of melt,mold and cooling water,speed of TZCC,and cooling distance.Outputs of the model are nine microstructure quantities,which are numbers of small grains within columnar grains,at the boundaries of the columnar grains,or at the surface of the alloy,the maximum and the minimum numbers of small grains within a columnar grain,numbers of columnar grains with or without small grains,and sizes of small grains and columnar grains.The model yields precise prediction,which lays foundation for controlling microstructures of alloys prepared by TZCC.