In order to improve the accuracy and efficiency of data flow classification detection, a new Gauss naive Bayes classification method based on weighted mechanism concept drift detection is proposed. Firstly, the proposed algorithm framework is designed, and the input data stream is used to establish the information table directly, and the Gauss naive Bayes classifier based on the information table is also constructed; Secondly, the Kappa statistical method is used to establish the concept drift detection method. According to the input data fluctuation, linear function and Bias function (nonlinear) are taken to detect the concept drift, and expert point deletion and information table are used to deal with the recurrent concept drift, to improve the drift detection accuracy and efficiency; Finally, simulation experiments show that the classification accuracy on the SEA test set, Hyperplane data set and SQD data set is 10.3 %, 16.8 % and 20.5 % higher than that of the contrast algorithm, which verifies the effectiveness of the classification algorithm.