The invention discloses a system for early detection and risk prediction of schizophrenia. The technical problem that currently, it is hard to accurately and objectively carry out early detection andrisk prediction on schizophrenia can be solved. The system comprises a test module, a collecting and preprocessing module, a data analysis module, a learning classification module and a classificationperformance evaluation method. According to the system for early detection and risk prediction of the schizophrenia, high-risk individuals, ultra-high-risk individuals and initially-attacked people of the schizophrenia are subjected to quantitative and objective detection recognition and risk prediction by comprehensively utilizing cognitive tests, clinical detection, P50 sensory gating electrophysiological characteristics and an XGBoost(the Extreme Gradient Boosting) machine learning method; specifically, by combinedly using cognition, clinic and P50 sensory gating experiments, and adoptingsource location, brain networks, machine learning and other analysis methods, initially-attacked people, high-risk people, ultra-high-risk people and normal contrast people are classified, and the classification accuracy, sensitivity and specificity are improved.