A method for prediction of key performance parameters of an aero-engine transition state acceleration process based on space reconstruction. Aero-engine transition state acceleration process test data provided by a research institute is used for establishing a training dataset and a testing dataset; dimension increase is conducted on the datasets based on the data space reconstruction of an auto-encoder; model parameters optimization is conducted by population optimization algorithms which is represented by particle swarm algorithm; and random forest regression algorithm performing well on high-dimensional data is used for carrying out regression on transition state performance parameters, which realizes effective real-time prediction from the perspective of engineering application.