A natural language understanding method, comprising: training a prediction model that comprises a first prediction model and at least one second prediction model, including: training the first prediction model based on first data comprising data for user language habits; and training the second prediction model by training a deep learning model based on second data comprising data for application fields; receiving a speech request and recognizing the speech request frame by frame to obtain at each moment a frame text, being a recognition result from a starting frame to a current frame, and a recognition text, wherein the recognition text is a recognition result of all frames from the starting frame to an end frame in the speech request; performing prediction on the frame text using the prediction model to obtain a prediction text during the frame-by-frame recognition, said performing prediction on the frame text using a prediction model to obtain a prediction text comprises: performing prediction on the frame text using the first prediction model to obtain a first prediction text; performing prediction on the frame text using the at least one second prediction model to obtain a second prediction text; and determining the prediction text according to the first prediction text and the second prediction text; processing the prediction text to obtain a first processing result; and comparing the recognition text with the prediction text after the frame-by-frame recognition is finished, and when the recognition text matches with the prediction text, outputting the first processing result as a natural language understanding result for the speech request. training a prediction model that comprises a first prediction model and at least one second prediction model, including: training the first prediction model based on first data comprising data for user language habits; and training the second prediction model by training a deep learning model based on second data comprising data for application fields; training the first prediction model based on first data comprising data for user language habits; and training the second prediction model by training a deep learning model based on second data comprising data for application fields; receiving a speech request and recognizing the speech request frame by frame to obtain at each moment a frame text, being a recognition result from a starting frame to a current frame, and a recognition text, wherein the recognition text is a recognition result of all frames from the starting frame to an end frame in the speech request; performing prediction on the frame text using the prediction model to obtain a prediction text during the frame-by-frame recognition, said performing prediction on the frame text using a prediction model to obtain a prediction text comprises: performing prediction on the frame text using the first prediction model to obtain a first prediction text; performing prediction on the frame text using the at least one second prediction model to obtain a second prediction text; and determining the prediction text according to the first prediction text and the second prediction text; performing prediction on the frame text using the first prediction model to obtain a first prediction text; performing prediction on the frame text using the at least one second prediction model to obtain a second prediction text; and determining the prediction text according to the first prediction text and the second prediction text; processing the prediction text to obtain a first processing result; and comparing the recognition text with the prediction text after the frame-by-frame recognition is finished, and when the recognition text matches with the prediction text, outputting the first processing result as a natural language understanding result for the speech request.