Abstract: Aiming at the problems of high false detection rate, high missed detection rate and long detection time of traditional methods, an anomaly detection method of UAV surveying and mapping data based on data screening is proposed. Support vector machine is used to preprocess the UAV mapping data, such as data flow blocking, vertical normalization processing and time slicing processing. Data is analyzed based on convolution neural network to determine the potential law of data. The unsupervised clustering algorithm is used to cluster the data, and the sliding window is used to get the cluster center factor of the data flow and cluster it. Block the abnormal data is rejected according to the judgment criteria to determine whether there are abnormal factors. The overlapping cumulative value calculation method is used to screen the abnormal data points to complete the anomaly detection of UAV mapping data. The experimental results show that compared with the traditional convolution neural network method, the false detection rate of abnormal data detection by this method is reduced by about 11%, and the missed detection rate is reduced by about 8.1%, and the detection time is shortened by 11.3 min.