The invention discloses a multi-source fusion-based pig important growth trait prediction method, and belongs to the technical field of animal growth trait prediction. The objective of the invention is to solve the problems of long time consumption and poor measurement result accuracy of the existing method for obtaining important growth traits such as backfat thickness and
eye muscle area. According to the method,
body size parameters and morphological characteristics of pigs are obtained based on
point cloud data corresponding to the pigs, then
body size parameter characteristics are adopted to enhance the morphological characteristics to obtain enhanced morphological characteristics, the enhanced morphological characteristics are sent to a Flaten layer to be flattened, and a second characteristic vector is obtained; obtaining a first
feature vector based on the pig
breed type
feature vector, the growth stage
feature vector, the slaughter day age and the exercise amount corresponding to the live pig; and the first feature vector and the second feature vector are spliced and then are sent to a neural
network model to obtain a predicted output important growth trait predicted value.