In face detection, the small target face carries less feature information and is relatively fuzzy, which leads to higher detection difficulty. In order to solve this problem, a novel algorithm was designed. The network that combines the single shot scale-invariant face detector (S3FD) network with the channel and the spatial attention mechanism was used as the backbone, and the channel and the spatial establish the weight relationship between the features, which strengthens the feature extraction ability. Then, the receptive field of the original S3FD output feature map was expanded and then up-sampled, so that the output of the feature map of the previous layer includes the features of the feature map of the next layer. Result: The average precision (AP) values of this algorithm on the three different levels of widerface verification datasets are 95.0%, 93.7%, and 86.4%, respectively, which are increased by 1.3%, 1.2%, and 0.5% compared with the original S3FD. The algorithm proposed in this paper has a better detection effect in face detection.