Abstract： In view of the cumbersome manual sleep staging process and the insufficient accuracy or the difficulty in interpreting classification results of automatic sleep staging model,we proposed an automatic sleep staging model based on mixed intelligence,which combined data intelligence and knowledge intelligence to achieve a balance of sleep staging accuracy,interpretability and gener-alization.Firstly,based on any combination of typical electroencephalography(EEG)and electrooculography(EOG)channels,a se-quential full convolutional network and multi-task feature mapping structure of U-Net architecture were constructed.Secondly,by com-bining different sleep map correction methods,the different action ways of knowledge intelligence to rough sleep map was explored.The F1 index of this model on the ISRUC and Sleep EDFx datasets were 0.804 and 0.780,respectively.In addition,the knowledge intelli-gence was used to the excessive jump in the rough sleep map and unreasonable transition of sleep stages.This research can provide an effective interpretive aid for sleep physicians,and has great potential in improving the efficiency of clinical sleep staging.