Research and Mining of Intelligent Home User Behavior Pattern Based on Machine Learning

Abstract

The behavior patterns of smart home users are mined accurately and effectively, so as to analyze the personalized behavior characteristics of smart home users, and then analyze the preferences of smart home users. The reliable and effective services are provided to users in a targeted manner. A method for mining the behavior patterns of smart home users is proposed based on machine learning. The characteristic data model of smart home user behavior pattern is constructed, and the association rule feature decomposition method is used to analyze the user behavior pattern and reconstruct the information. According to the difference of user behavior pattern big data, the characteristics of directional behavior are analyzed, the characteristics are classified according to the user's behavior preference and information fusion, a user behavior model is set up with big data classification model. According to the behavior characteristics of users, the intelligent decision and judgment are realized, and the convergence control is carried out by using machine learning algorithm, which improves the self-adaptability of user behavior pattern mining. The simulation results show that the proposed method is accurate and convergent in behavior pattern mining of smart home users.