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Traffic flow data analysis is the basis for implementation of traffic planning, control, and management. Abnormal traffic data which is not conducive to all aspects of transport research and related work brings difficulties to the identification of traffic conditions, traffic management and control. Therefore, it is necessary to repair abnormal data. To improve the recovery accuracy of traffic flow anomaly data and further improve the quality of traffic data, a model of traffic flow anomaly data recovery method based on improved K-Nearest Neighbor(KNN) algorithm was constructed. The model is improved by optimizing the k-values and state vectors in the KNN basic model, and proposing a distance weights as the selection of neighboring weights values. In order to verify the validity of the model, the measured traffic flow data was used for experimental analysis. The experimental results show that the improved KNN data recovery model has higher recovery accuracy, the mean average relative error is 9.88%. It can effectively improve the data quality, and provide basic data support for the intelligent traffic control system.