Pavement anomalies can bring inconvenience to drivers and passengers，and even cause traffic acci-dents． A pavement anomaly detection algorithm based on sensor time series data was proposed． Considering the problem that sensing signals collected during driving are strongly high-order sequential correlative，high-order dy-namic Bayesian network classifier was constructed to realize the anomalies detection． Firstly，correlation analysis and Granger causality test were used to initialize the network structure． Secondly，the sensing signals were decom-posed by wavelet transform，and convolution neural network was used to realize network embedding． Finally，link prediction with minimal description length was used to optimize the network structure． The results show that，com-pared with the traditional method of time series classification，the proposed method can reduce fallout rate and missing rate，and increase F1 score on the sequential correlative signals，and thus is more robust．