The invention discloses a turbine backpressure trend prediction method based on a catboost algorithm, and relates to the field of machine learning, and the method comprises the following steps: S1, preparing historical operation data, S2, carrying out the preprocessing of the historical operation data, S3, carrying out the feature engineering dimension raising of the preprocessing data, S4, carrying out the re-sampling and screening of variables having a large correlation degree with the backpressure, S5, determining parameters, carrying out the modeling through catboost, and carrying out the prediction of the backpressure trend of a turbine. The screened historical operation data predicts the back pressure in a certain period of time, and the model trained in the step S6 is used for predicting and replacing with the model trained offline when the error of the current training preparation model and the current prediction model is too large at the same time; the method is indirect and easy to operate, can effectively predict the back pressure of the steam turbine, enables the prediction model and offline training to be parallel, can make up the error of the prediction model in time, and improves the prediction precision.