This paper adopts machine learning methods to train the kdd-cup99 network intrusion detection data set and analyze the results. Python programming is used to implement three supervised learning methods such as “naive Bayes classifier”, “Softmax regression” and “decision tree regression”. Firstly, the three classifier library functions are used to analyze and predict the KDD-CUP99 data set. Then, the incremental training method is used to explore the dependence of three classifiers on the amount of training data. Finally, feature screening is adopted to explore the influence of the number of sample features on the three classifier algorithms.