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Parkinson's disease is one of the most common neurodegenerative diseases, with clinical features overlapping with other neurodegenerative diseases and a lack of precise pathological mechanisms, leading to difficulties in early diagnosis and high misdiagnosis rates.; In order to study effective early detection methods for Parkinson's disease, deeply explore the temporal characteristics of Parkinson's disease development, and improve the accuracy of early Parkinson's disease prediction, analysis, and diagnostic decision-making, a multimodal detection system for early Parkinson's disease based on temporal convolutional networks was designed, providing an auxiliary diagnostic basis for the timely detection of early Parkinson's disease; The system utilizes speech, gait, and subject self-test data, adopts multiple linear pooling methods for multimodal fusion, and combines time convolutional networks and parameter sharing methods to improve the detection accuracy of the system and reduce overfitting risks; The results of ablation and comparative experiments show that the accuracy of the early Parkinson's disease detection system based on temporal convolutional networks reaches 96.22%, which is superior to traditional Parkinson's detection models in multiple evaluation indicators and demonstrates good early Parkinson's joint detection performance.