Video anomaly event detection and localization aim to detect abnormal events and lock its localization in the video. However， the video scenes are complex and diverse， and the localizations where anomaly events occur are random and changeable， which makes it difficult to accurately locate the occurred abnormal events. This paper proposes a video anomaly event detection and localization method based on convolutional auto-encoder patch learning. Firstly， we divide the video frames evenly into patches and extract the optical flow and the histogram of oriented gradient （HOG） feature of each patch. Then， at the different patches in the video， we individually design a convolutional auto-encoder to learn the feature in the normal motion mode. During the anomaly event detection process， the reconstruction loss of the convolutional auto-encoder is used for anomaly detection. The proposed method can effectively perform feature learning for different regions of the video and improve the accuracy of anomaly event localization. Experimental results on three public datasets， UCSD Ped1， UCSD Ped2， and CUHK Avenue， demonstrate that the frame level AUC （area under the curve） of this method is increased by 5.61% on average and can accurately locate anomaly events.