User feature extraction and identity authentication methods based on interactive behavior are an important method of identity recognition. However, for high-frequency users, the interactive behavior patterns and operating habits are relatively stable, which are easily imitated by fraudsters and making the existing models have a higher misjudgment. The key point to solve the above problems is to make the user's behavior change smoothly and distinguishably. This study proposes a smooth intervention model based on an individual interactive behavior system to handle it. Firstly, according to the user history web behavior log, the change trend of user interaction behavior is obtained from multiple dimensions. Then, combined with the stability and deviation of the behavior, the time-domain drift algorithm (TDDA) is proposed to determine the behavior guidance time of each user. Finally, an intervention model for interactive behavior reconstruction systems is proposed, which superimposes behavior trigger factors on non-critical paths in the system to guide users generating new interactive behavior habits. Experiments prove that the method proposed in this study could guide the user's behavior to change smoothly and produce sufficient distinction to significantly advance the model accuracy in the scenario of behavior camouflage anomaly detection.