Because there are few visual features of small targets and it is difficult to locate them, many small object detection methods are based on Feature Pyramid Network (FPN) to perform multi-scale fusion to enrich the information of each feature layer. However, FPN only focuses on the local correlation of features and uses element-wise addition operations to fuse different feature layers, ignoring the differences in the receptive fields of different feature layers. Therefore, the Enhance Context Feature Pyramid Network (ECFPN) is proposed, the Context Information Enhancement (CIE) is designed to enhance context information, and the Attention Guided Feature Fusion (AGFF) fuses high-level feature maps and low-level feature maps. The experimental results show that the ECFPN has AP0.5 and APS of 75.05% and 19.48% respectively on VOC2012 dataset, and of 93.48% and 45% respectively on NWPU VHR-10 dataset, which has good small target detection performance.