Parkinson’s disease (PD) affects nearly 10 million people worldwide, and there is no cure, but evidence-based medicine suggests that training based on sensory cues can slow disease progression. However, most current studies are based on a single modality and lack user perception and feedback. This study proposes an audiovisual collaborative multimodal gait training method, which generates and dynamically adjusts multimodal cues based on users’ gait data to investigate the feasibility of assisting early-stage PD rehabilitation. The method constructs a multimodal cue generation framework to generate visual and auditory cues by calculating cycle and step height parameters from gait data. Then, an interactive intervention training system is built to dynamically adjust the audiovisual cues based on gait changes, which realizes the interactive iteration between user perception and multimodal cue generation. Finally, 40 patients with early-stage PD (H&Y stage≤2) are recruited for a clinical experiment. Compared with the control group, the audiovisual synergy group shows the best improvement effect. Compared with baseline, the gait symmetry in the audiovisual synergy group increases by an average of 20.776% (p=0.0001) during the training and 21.157% (p=0.0001) after the training, and the velocity on the affected side increases by an average of 33.924% (p=0.0001) during the training and 36.433% (p<0.0001) after the training. The results indicate that audiovisual synergistic cues can help patients improve gait performance more quickly and sustainably. The proposed multimodal cueing training method based on gait data provides a new approach for the establishment of a quantitatively driven precision rehabilitation model, and promotes the application and development of multimodal interaction technology in the medical field.