The invention discloses a speculative multithreading division method based on machine learning. The speculative multithreading division method comprises the following steps: extracting program characteristics from an irregular program set, and combining a CFG (Control Flow Graph) with comments with a key path to show the program characteristics; then, constructing a program CFG by a SUIF compiler, converting the program CFG into a weighted CFG and a super block CFG, carrying out threading division, which aims at a cyclic part and an acyclic part, on the program set to obtain a training sample set formed by the program characteristics and an optimal division scheme; and finally, extracting the characteristics of an irregular program to be divided, calculating similarity between the characteristics of the irregular program to be divided and the program characteristics in the training samples, and carrying out weighted calculation on the division threshold values of a plurality of most similar sample programs to obtain an optimal division scheme suitable for the irregular program. The similarity between the program to be divided and the sample program is compared on the basis of the program characteristics, a similar sample division scheme is applied to the program to be divided, and therefore, the speculative multithreading division method exhibits better adaptability on each class of parallel irregular programs.