With the evolution of next-generation network technologies, the complexity of network management has significantly increased, and the means of network attacks are diversified, bringing new challenges to network traffic classification. This paper presents a general AI-driven network traffic classification workflow and elaborates on a traffic data and feature engineering framework. Most importantly, it analyzes the concept and causes of data distribution shifts in network traffic, proposing detection methods and countermeasures. Experimental results on real traffic collected at different time intervals show that application evolution can induce data distribution shifts, which in turn lead to a noticeable degradation in traffic classification performance. Comparative drift detection experiments further confirm that such shifts are more evident over long-term intervals, while short-term traffic remains relatively stable. These findings demonstrate the necessity of incorporating drift-aware mechanisms into AI-driven network traffic classification systems.