This research explores the integration of Artificial Intelligence (AI) and Big Data into public health campaigns, envisioning a future where precision, personalization, and proactive interventions redefine healthcare. Analyzing the transformative potential and challenges, the study examines AI’s role in disease surveillance, diagnostics, and predictive modeling, alongside Big Data’s contributions to personalized interventions and comprehensive health understanding. Ethical considerations, the digital divide, and regulatory frameworks are central challenges, necessitating a delicate balance between innovation and responsibility. The conclusion foresees a healthcare landscape where AI and Big Data enhance the effectiveness of public health campaigns, promising a future characterized by equitable, data-driven, and resilient approaches to address emerging health challenges.

This thesis explores the force exerted by victims on criminals and crime phenomena based on the theory of “interactive relationship”, providing a new perspective for the study of crime risk and crime formation. Through researching and applying information extraction technology, a knowledge graph of the interaction risk characteristics between home invasion theft offenders and victims is constructed, which serves as a reference for comprehensively, accurately, and dynamically understanding the formation of crime risk. This enables those in charge of crime control to anticipate the interactive patterns of such crime risks in advance, so as to intervene and control the crime risk before the crime fact emerges.