The invention discloses a layered integrated Gaussian process regression soft measurement modeling method used for a complex changeable multi-stage chemical process. The layered integrated Gaussian process regression soft measurement modeling method is an on-line multi-model strategy. A Gaussian mixture model is employed to identify different stages of the process, principal component analysis is carried out on data in each stage, on the basis of the contribution degree of each auxiliary variable in the principal element space, data in each mode is divided into several subspaces, and a corresponding Gaussian process regression soft measurement model is established. When new data comes around, variable selection is carried out by means of subspace PCA, and on the basis of the soft measurement model which is established off line, the prediction output of each model can be obtained. By carrying out mean value fusion on outputs of subspace models, first layer integrated output, i.e., local prediction output in each mode can be obtained, finally new data obtained according to calculation is attached to the posterior probability of each different stage, and local prediction in each mode is fused by means of the posterior probability to obtain second layer integrated output. Key variables can be accurately predicted, and therefore the product quality is improved, and the production cost is reduced.