The invention discloses a Parkinson's disease early warning system based on daily behavior analysis, relates to the technical field of computer vision and deep learning, and comprises a video acquisition module, a video analysis module, a controller, a database, a processing terminal, a behavior analysis module, an alarm module and a specific behavior task module. According to the invention, a multi-azimuth high-spatial-resolution camera is adopted, computer vision and a deep learning technology are utilized, other hardware equipment is not needed, and behavior characteristics of TIA related symptoms, such as sleep disorder, balance loss, cognitive dysfunction and dyskinesia, are accurately captured by carrying out recognition tracking and skeleton analysis on 19 joint points of the trunk of a person, so that daily activity behavior capture and specific behavior task module scoring analysis are realized, and are compared with normal daily behaviors and behavior scoring values to objectively judge whether a testee suffers from the risk of Parkinson's disease or not; and then the effect of early warning of patients suffering from the Parkinson's disease is achieved.