ENSO forecasting method based on deep learning

The invention provides an ENSO forecasting method based on deep learning. The ENSO forecasting method comprises the following steps: acquiring a plurality of physical element data observation sets from different ways; processing the physical element data observation sets, so that the data formats of all the physical element data observation sets are the same; pre-extracting a plurality of physicalelement fields in the physical element data observation set to obtain training features; through a deep learning algorithm, importing each element physical quantity in the training features into themodel for training to obtain a forecasting model; and importing the real-time observation data into the forecasting model to obtain a forecasting result. According to the ENSO forecasting method basedon deep learning provided by the invention, effective information hidden in a large amount of data is mined to the greatest extent in a manner of combining deep learning, and the problem of relatively large ENSO forecasting deviation in the prior art is effectively solved, so that more accurate forecasting is realized, and the forecasting precision is improved.