In this study, traffic flow forecasting in the field of traffic data mining is studied and implemented. This paper presents an algorithm for feature selection of traffic flow data and establishment of traffic flow prediction model based on data mining technology. After cleaning the sampled data, the classification and regression decision tree are used as base learners, and the gradient lifting decision tree is used for regression fitting to calculate the characteristic importance of traffic data. The importance is used as the basis of adaptive feature selection. Secondly, the clustering algorithm is used to cluster the selected feature data, which reduces the sample size and makes the similar data more similar. Finally, real-time data matching and clustering are used as training data sets, and support vector machine is used to predict traffic flow after parameters optimization by Artificial Fish Swarm Algorithm (AFSA). At the end of this paper, experimental data are presented to demonstrate the proposed algorithm and model.