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In order to satisfy the real time demand of traffic control and guidance system, reduce the occurrence of traffic congestion, reduce the frequency of traffic accident emergency, need to forecast the short-term traffic flow. Current short-term traffic flow prediction method is using K - nearest nonparametric regression to forecast and predict the process of no will be key factors in the prediction model of traffic flow in detail, the influence of lead to inaccurate prediction results, the problems of short-term traffic flow prediction error is bigger. For this, put forward a kind of short-term traffic flow prediction method based on fuzzy neural network. This method firstly on the basis of the history of short-term traffic flow data sample series, the extracted correlation dimension as a short-term traffic flow of the chaos characteristics, and then based on the characteristics, the clustering of the short-term traffic flow data and make the same short-term traffic flow aggregation class samples than the aggregation of different traffic flow class samples more press close to, by using the Gaussian process regression of short-term traffic flow forecasting model, using the finite difference method in the process of construction of short-term traffic flow forecasting sequences with smooth operation, after training for short-term traffic flow prediction model, introducing the Gaussian model to short-term traffic flow prediction in the process, get the traffic flow forecasting variance, and traffic flow prediction confidence interval were determined, thus realizing short-term traffic flow prediction. The realization of short-term traffic flow prediction. The experimental results show that the proposed method can accurately predict the transportation system of the real-time condition, the best way for vehicle is the effective guidance, reduces the impact on natural and human factors interference, the result of the short-term traffic flow prediction for the traffic department to provide a basis for the control of road traffic management.