Lightweight face detection algorithms that typically utilize convolutional neural network to find out all faces from the entire vision range. However, compared with more accurate and heavy algorithms, the performance of existing lightweight networks is still left far behind. Toward this end, we propose a lightweight and efficient single-stage face detector, named ACWFace, which explores the effects of attention, context module, and weighted feature fusion based on RetinaFace. First, efficient dual attention module is designed to further explore the potential of channel attention and spatial attention by introducing adaptive convolution kernel. Second, extended context module and shuffled context module are proposed to enlarge the receptive field and increase the information intersection between branches. Finally, weighted-fusion feature pyramid network is utilized to solve the features fusion of different scales equally by introducing the feature fusion module. Experiments on the easy, medium, and hard datasets of WIDER FACE validation partition show that our ACWFace outperforms RetinaFace average precision by 1.0%, 1.1%, and 1.8% while it achieves a great growth of 0.6%, 6.5%, and 3.0% on annotated faces in the wild, PASCAL face, and face detection data set and benchmark datasets, respectively.