Gait recognition is one of the most important biometric technologies and 3D convolutional neural networks (CNNs) has achieved great success in this field. However, most existing gait recognition frameworks based on 3D CNNs only extract gait features from a single temporal scale, which may not pays enough attention to the gait information in different scales. To solve this problem, we propose a novel multi-scale temporal information extractor to aggregate temporal information from different scales and then represent gait features comprehensively. The small-temporal-scale branch extracts the temporal features from the adjacent frames, which contains the information of slow changes, while the larger-temporal-scale one is used to capture the rapid gait changes. Experiments demonstrate that the proposed method outperforms most existing gait recognition methods on CASIA-B and OutdoorGait datasets.