Aiming at the problem of low detection accuracy of occluded faces in real scenes， an occluded face detection method based on convergent convolutional neural network （CNN） and attention enhancement network was proposed. First， on the multi-layer original feature map of the main network， the response value of the visible part of the face in the original feature map is enhanced by supervised learning. Then， multiple enhanced feature maps are combined into an additional enhanced network and set in converge with the main network to accelerate the detection of multi-scale occlusion faces. Finally， supervised information is distributed to feature maps of various sizes for supervised learning， and loss functions based on anchor frame sizes are set for feature maps of different sizes. Experimental results on WIDER FACE and MAFA datasets show that the detection accuracy of the proposed method is higher than the current mainstream face detection methods.