The application provides an
analysis method for complex evolutionary history based on
deep learning, comprising: according to a preset species evolutionary history
simulation sequence data, respectively as a model
training set and a
test set; determining the topological structure of the
training set data, and labeling the training data in different topological structure proportions; constructing a
convolutional neural network, training and testing the
convolutional neural network with the
data set, so that the error between the
data prediction value and the
label value is minimized; based on the trained
convolutional neural network, analyzing real genomic sequence data, and combining other
population genetics analysis methods, determining the evolutionary relationship and
introgression site between different biological groups. The application uses comparative
genome or
population genome data, infers the topological structure between sequences through a
deep learning algorithm, further evaluates the evolutionary relationship at the
genome level, and identifies local
introgression signals through the difference of the topological structure between different regions.