Taking ship traffic flow as the research object,  a Gaussiana gated recurrent unit (Gaussiana GRU) model based on the Gaussian distribution assumption was proposed to predict the uncertainty distribution of ship traffic flow parameters. Furthermore, a joint probability prediction method for traffic flow density and speed was established by combining with the Copula function. Firstly, based on the characteristics of traffic flow,  a residual GRU structure was designed to enhance the feature extraction capability of GRU through deep stacking, and the Gaussian likelihood function was combined to estimate the probability density distribution of traffic flow. Secondly, in order to solve the "lag" problem of prediction in uncertainty prediction, a point value processing module was introduced to improve the stability and accuracy of model prediction. The joint probability model of traffic flow density and speed was established by using Gaussian Copula function, and the state of three waterways in the Fujiangsha water area were estimated by using sampling method. Experimental results show that compared with the existing models, this method performs well in both point value  and probability density prediction, and can quantify the uncertainty characteristics of ship traffic flow more accurately.