The invention discloses an ancestor polymorphism prediction method based on a big data artificial intelligence algorithm, which comprises the following steps of: A, constructing a training set according to population genome data; B, performing gene orientation on the group samples of the training set, and performing 1 and -1 encoding on the two haplotypes after orientation, and performing window division on the genome at the same time; c, selecting an optimal classifier through a voting strategy to form a window observation sequence, and taking the result of the classifier as the next step ofinput; d, constructing a transfer matrix and an emission matrix of the window, and establishing a hidden Markov model; and E, predicting probability distribution of a hidden state through the hidden Markov model, solving an optimal ancestor source result label, and outputting the optimal ancestor source result label as a final result. Through the method, the defect that an existing population polymorphism method has an important effect on genetic population research and positioning of some population-associated disease genome fragments can be overcome, and meanwhile the polymorphism of the chromosome genome fragments can be finely predicted.