Predicting interests and making reasonable recommendations based on user browsing records and other information has become a common means for many sales platforms to optimize the user experience. Thus, the issue of user information security has naturally become a major challenge for major platforms. This paper proposes an endogenous securitybased consumer behavior data collection and analysis platform, which accurately predicts future sales traffic data by collecting user data and using a prediction model based on long and shortterm memory networks. In terms of data security, the platform uses endogenous securitybased mimetic cloud WAF, providing autonomous and controllable security for the entire data platform through three core technologies: dynamic selection algorithm, heterogeneous executables, and adjudication algorithm, and detects anomalous traffic by utilizing sketchbased network measurement techniques. In addition, the platform incorporates data backup and recovery, encrypted storage, and data transmission encryption technologies, and takes measures such as categorized storage and access control for important data. Extensive experiments demonstrate that the prediction platform used for China Tobacco’s sales traffic has significant improvement in prediction accuracy and data security when compared with existing techniques, and can provide a reasonable and feasible solution for enterprise sales prediction.
