In order to solve the problems of feature selection dependent on experience and poor robustness caused by outliers in machine learning traffic anomaly detection, a fundamental point classification method for traffic anomaly detection based on the “background relation-background distribution-background basis” system by factor space theory is proposed. Firstly, the KNN outlier detection algorithm is used to remove outliers in the data in the data preprocessing stage to reduce the influence of outliers on the subsequent background basis extraction. Secondly, the mRMR algorithm is used to sort the data features and select the most influential features for classification as category distinguishing features. Then, the background basis extraction algorithm is optimized based on the internal point discriminant method, and the background basis of different types of data in the training data is extracted, and the unit cognition package of each type is obtained. Finally, a fundamental point classification algorithm (FPCA) based on the unit cognitive packet is constructed to achieve accurate two-class classification of abnormal traffic. The proposed method attains accuracy rate of 92.48% and F1-score of 92.18% in a two-class classification task on the NSL-KDD dataset, which detection performance superior to the same type machine learning method. The test on CICIDS2017 scene data set further verifies the feasibility of the proposed method.