The identification and prediction of real-time traffic conditions rely on data processing. Abnormal data recognition in traffic big data uses machine learning methods with multi-source traffic to ensure the accuracy of traffic detection data. The recognition of anomaly detection data is based on AdaBoost method in machine learning. To eliminate the outlier phenomenon of the single detection source data, the training dataset of the training process selected datasets provided by multiple detection sources on the same road section. The cost-sensitive method optimizes the decision-making process of the improved algorithm. Experimental results show that the improved AdaBoost model forced the classifier to pay more attention to abnormal class samples, which enhanced the representation of training decision tree rules in the AdaBoost and improved the classification accuracy of abnormal samples. The highway test dataset verified the detection accuracy, false detection rate, false alarm rate, and other indicators of the improved algorithm and related classical algorithms. The accuracy rate of the improved algorithm was increased by 5.547%, and the false detection rate was reduced by 6.792%. The comparison of ROC curves shows that the improved AdaBoost method is more reliable in identifying abnormal samples of traffic detection and can effectively adjust the classification error caused by non-equilibrium data.