Optical coherence tomography (OCT) is widely used in ophthalmology to observe the morphology of the retina, and is of great significance for the detection and diagnostic evaluation of lesions. Due to series of retinal diseases caused by liquid accumulation, a neural network with global context feature information is designed for liquid detection and segmentation in retinal OCT images. By means of multi-scale feature extraction and fusion,a multi-scale parallel extraction and highly integrated U network model PH-UNet is proposed, which is a new deep convolutional network for liquid area segmentation in OCT images. PH-UNet network captures multi-scale contextual information, and better utilizes information extraction and fusion methods to perform end-to-end segmentation of the liquid area of the OCT image. The proposed model is segmented on three types of retinal fluid. The intraretinal fluid (irf), subretinal fluid (srf), and pigment epithelial detachment (ped) are segmented and compared with other classic segmentation network models. The best results have been achieved on the three indexes of precision,dice and mIoU, which proves its superiority.