Parkinson's disease is a neurological disorder, and early diagnosis is crucial for the treatment and quality of life of patients. Gait movement disorder is a significant manifestation of PD, and automated gait assessment is key to achieving automated detection of PD patients. With the development of deep learning, in order to improve the accuracy of early Parkinson's disease detection and enhance the robustness of motion recognition models, this study introduces an innovative deep learning approach, namely Multi-area Attention Spatiotemporal Directed Graph Convolutional Network (Ma-ST-DGN). The model effectively captures temporal and spatial information from the movement data of subjects to better understand subtle movement abnormalities in patients. Simultaneously, by reconstructing human skeleton features using directed graphs and introducing a multi-area self-attention mechanism, the model can adaptively focus on key information in different areas and apply more effective fusion strategies on features from different areas, thereby increasing sensitivity to potential signs of Parkinson's disease. By more effectively integrating global and local area information, the model captures subtle manifestations of PD. We use the first Parkinson's disease gait dataset, PD-Walk, consisting of walking videos of 95 PD patients and 96 healthy individuals. Extensive experiments on this clinical video dataset demonstrate that the model achieves the best performance to date, with an accuracy of 88.7%, far superior to existing sensor and vision-based Parkinson's gait assessment methods. Therefore, the method proposed in this study may be effective for early diagnosis of PD in clinical practice.