Based on the single shot multibox detector （SSD） single-stage face detection model， this paper proposes a multi-size occlusion face detection method based on a hierarchical attention enhancement network to solve the problem of poor accuracy of face detection under complex partial occlusion. Firstly， on the multi-layer original feature map of SSD basic network， the attention enhancement mechanism is introduced to improve the response value of the visible region of the face. Then， different anchor sizes are designed for different enhancement feature layers to improve the hierarchical recognition effect of multi-scale occluded face. In training， the attention loss function， the classification loss function and the regression loss function are fused into a multi-task loss function to jointly optimize the network parameters. Experiments on the WIDER FACE dataset and the MAFA occlusion face dataset show that the detection accuracy and timeliness of the method are better than those of the current mainstream occlusion face detection methods.