Abstract: Aiming at the periodic dynamic characteristics of traffic flow, a probabilistic combination model of traffic flow based on similarity clustering is proposed, which fully excavates the similarity characteristics of traffic flow in different periods. Firstly, the adaptive k-means + + clustering method is used to cluster the historical traffic flow data and classify the traffic flow data with time similarity. Then, the combination model is constructed for different sequence feature data sets. Furthermore, according to the new traffic flow state data, the similarity between the new traffic flow state data and the classified data is analyzed, and the probability weight of the combined model is calculated. Then, the prediction output is obtained by fusing the probability weight of the combined model results. Finally, the validity and accuracy of the proposed prediction model are verified by simulation experiments.