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Video anomaly detection is a significant problem in computer vision tasks. It asks methods to detect unusual events in videos. The kernel of this task is to produce a correct understanding of the input video. To achieve this target, both spatial and temporal features are needed to be extracted by methods. Based on the research of image processing, the deep convolutional neural networks have been evaluated that they have good performance on the spatial feature extraction. Thus, the problem becomes how to get temporal features in the video. This paper proposes a model that combine two effective temporal features processing methods, Convolution 3D and Long Shortterm Memory to handle the video anomaly detection. We do experiments on a famous video anomaly dataset, UCF-crime, and achieve a better performance compared with other methods.