The present invention provides a prediction method for stall and surging of an axial-flow compressor based on a deep autoregressive network. Firstly, selecting and preprocessing surging experimental data of a certain type of aero-engine, and dividing the data into a training set and a test set. Secondly, building and training a deep autoregressive network model based on an attention mechanism, using the finally trained model to conduct real-time prediction on the test set, and giving a model loss and an evaluation index. Finally, using a prediction model to conduct real-time prediction on the test data, and giving a trend of surging probability varying with time in chronological order. The present invention uses the attention mechanism to effectively capture the features of the experimental data and accurately predict the surging probability, which improve the stability and accuracy of prediction, is beneficial to improving the performance of active control of the engine.