Research on Early Identification and Early Warning of Children's Mental Health Problems Based on Big Data

Abstract

In recent years, children's mental health issues have attracted attention from all sectors of society. Traditional identification and intervention models often rely on manual observation and questionnaires, which are inefficient and susceptible to subjective interference. With the rise of big data technology, early warning systems based on multisource heterogeneous data have become a research hotspot. This article proposes an early identification and early warning system architecture for children's mental health problems that integrates multidimensional information such as behavioral data, social network data, and physiological signals. The system adopts advanced data fusion and deep learning methods to achieve individual, differentiated risk assessment for large-scale child populations.