The present application relates to the field of
medical information processing, and particularly refers to an orthopedic risk
intelligent decision system based on multi-
modal bioelectric
signal fusion, comprising an intelligent recognition module, an individual physiological atlas dynamic modeling module, a decision reasoning module, a dynamic evaluation optimization module and a
risk prevention module, the present application constructs an individual physiological atlas based on a graph neural network, fuses historical physiological data of a user with real-time multi-
modal bioelectric signals for modeling, realizes dynamic representation of individual physiological states, effectively depicts individual difference characteristics of the user, and thus significantly improves the accuracy of abnormal physiological
state recognition; by introducing a pre-clustering mechanism and a
muscle-bone collaborative fan-shaped topological subgraph structure, combining a graph neural
network model of local neighborhood aggregation and long-distance similar neighborhood aggregation, multi-scale modeling of complex correlation relationships in multi-
modal physiological data across regions and modalities is realized, and the stability and reliability of risk prediction are effectively improved.