Person re-identification aims at pedestrian target matching under distributed monitoring systems. Compact and robust feature is critical to it. For this reason, this study proposes a feature extraction method based on feature fusion network. Firstly, the STEL algorithm is used to enhance the immunity of LOMO feature to background noise, and the KPCA algorithm is used to reduce dimension. Subsequently, we explore the complementarity between manual features and Convolutional Neural Network (CNN) features, and integrate the improved LOMO feature into the CNN to obtain a fusion feature with better performance. Experiments on two datasets (VIPeR and CUHK01) validate the effectiveness of our proposal, the Rank-1 of fusion feature is 3.73% and 2.36% higher than the cascade feature, respectively.