Considering the large change of trajectory prediction error caused by the change of road traffic flow at different time, a Gauss Mixture Time Series Model (GMTSM) based on probability distribution model was proposed. The model regression of mass vehicle historical trajectories and the analysis of road traffic flow were carried out to realize vehicle trajectory prediction. Firstly, aiming at the problem that the uniform grid partition method was easy to cause the splitting of related trajectory points, an iterative grid partition method was proposed to realize the quantity balance of trajectory points. Secondly, Gaussian Mixture Model (GMM) and AutoRegressive Integrated Moving Average model (ARIMA) in time series analysis were trained and combined together. Thirdly, in order to avoid the interference of the instability of GMTSM hybrid model's sub-models on the prediction results, the weights of sub-models were dynamically calculated by analyzing the prediction errors of the sub-models. Finally, based on the dynamic weight, the sub-models were combined together to realize trajectory prediction. Experimental results show that the average prediction accuracy of GMTSM is 92.3% in the case of sudden change of road traffic flow. Compared with Gauss mixed model and Markov model under the same parameters, GMTSM has prediction accuracy increased by about 55%. GMTSM can not only accurately predict vehicle trajectory under normal circumstances, but also effectively improve the accuracy of trajectory prediction under road traffic flow changes, which is applicable to the real road environment.