Deep learning has further promoted the research of gait recognition algorithms， but there are still some problems， such as ignoring the detailed information extracted by shallow networks， and difficulty in fusing unlimited time-space information of gait videos. In order to effectively utilize shallow features and fuse time-space features， a cross-view gait recognition algorithm based on multi-layer refined feature fusion was proposed. The proposed algorithm was consisted of two parts： Edge Motion Capture Module （EMCM） was used to extract edge motion features containing temporal information， Multi-layer refined Feature Extraction Module （MFEM） was used to extract multi-layer fine features containing global and local information at different granularities. Firstly， EMCM and MFEM were used to extract multi-layer fine features and edge motion features. Then， the features extracted from the two modules were fused to obtain discriminative gait features. Finally， comparative experiments were conducted in multiple scenarios on the public datasets CASIA-B and OU-MVLP. The average recognition accuracy on CASIA-B can reach 89.9%， which is improved by 1.1 percentage points compared with GaitPart. The average recognition accuracy is improved by 3.0 percentage points over GaitSet in the 90-degree view of the OU-MVLP dataset. The proposed algorithm can effectively improve the accuracy of gait recognition in many situations.