Research on Multi-Source Data Fusion Monitoring and Early Warning Model for Children's Mental Health Based on Urban Rural Differences

In recent years, the continuous development of social and economic factors has led to a significant gap between urban and rural areas, which has led to new changes in the map of children's mental health in China. The traditional mental health monitoring system and early warning model are not adequate to deeply analyze the characteristics of children's psychological changes. To address this gap, this study uses a multi-source data fusion algorithm, taking urban and rural children as the research object, and uses binary logistic regression to analyze the influencing factors of children's mental health changes. In order to solve the problem of a too-large quantity of sample data, a multi-source data cluster of children's mental health is constructed, and daily behavior data are collected to reduce the influence of subjectivity. An early warning model of children's mental health based on Temporal-Behavioral Stream Encoding Network is proposed, which captures the dependence of psychological change characteristics, realizes deep nonlinear change, and completes data modeling.