Aiming at the problems of color distortion, noise amplification, and loss of detailed information in the process of low illumination image enhancement, a progressive fusion of parallel hybrid attention (PFA) is proposed. First, a multi-scale weighted aggregation (MWA) network is designed to aggregate multi-scale features learned from different receptive fields, promote the global representation of local features, and strengthen the retention of original image details; Secondly, a parallel hybrid attention module (PHA) is proposed. Pixel attention and channel attention are combined in parallel to alleviate the color difference caused by the distribution lag of different branches of attention, and the information between adjacent attention is used to complement each other to effectively improve the color representation of images and reduce noise; Finally, a progressive feature fusion module (PFM) is designed to reprocess the input features of the previous stage from coarse to fine in three stages, supplement the shallow feature loss caused by the increase of network depth, and avoid the information redundancy caused by single stage feature stacking. The experimental results on LOL, DICM, MEF, and LIME datasets show that the performance of the method in this paper is better than that of the comparison methods on multiple evaluation indicators.