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This invention relates to the field of medical signal processing technology, specifically disclosing an AI-assisted diagnostic method and system for Parkinson's disease based on brain networks. The method includes: acquiring continuously collected temporal signals from the brain and establishing a discrete sequence; establishing an effective window set through signal activity screening; evaluating the temporal connectivity of the effective window set to obtain a steady-state data stream; analyzing the phase synchronization index and determining significant functional coupling to construct a topological confidence structure of the brain network; establishing an equivalent resistance model based on the topological confidence structure, outputting global conductivity, and tracking potential energy drops to establish a transmission cost subgraph; and performing gridded density analysis based on the transmission cost subgraph to obtain spatial damping distribution and evaluate the brain network's operational state. The system includes: a signal acquisition module, a temporal evaluation module, a confidence analysis module, a blockage analysis module, and a state evaluation module. This invention is beneficial for assisting in the diagnosis of Parkinson's disease and providing data support.