The invention discloses a fatigue crack growth prediction method based on an improved particle filter algorithm, comprising the following steps: A, defining a state model and an observation model; B,transfering the model parameters; C, carrying out crack state transfer; D, when there is a new crack monitoring value, the particle value being brought into the observed likelihood probability densityfor calculation, and the normalized weight value of the particle being obtained; the posterior distribution of crack length and the posterior distribution of model parameters being obtained; E, the parameter of the state model being taken as the propagation of the crack length to obtain a new particle set of the crack length and the model parameters; F, the crack length and the model parameter particle set being brought into the state transfer equation to realize the prediction of the crack development trend, and the probability distribution of the crack length at any time being obtained; fora given crack length threshold, the probability distribution of residual life at any time being calculated. By adopting the invention, the convergence speed of the parameters can be improved and theprediction accuracy can be improved through the parameter transfer process of the new model.