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 of the current studies are based on a single modality and lack user sensing and feedback. Here we propose an audiovisual 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 the cycle and step height parameters from user gait data; then an interactive intervention training system is built to dynamically adjust the audiovisual cues based on the user's gait changes, which realizes the interactive iteration between the user's perception and the multimodal cue generation. Finally, 40 patients with early-stage PD (H&Y ≤2) were recruited for the experiment in the clinic. Compared with the control group, the audiovisual synergy group had the best improvement effect. Compared with the baseline, the symmetry of gait in the audiovisual synergy group increased 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 increased 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 coordinated cues can help patients improve gait performance more quickly and sustainably. The proposed multimodal cueing training method based on gait data provides a new idea for the establishment of a quantitatively-driven accurate rehabilitation model, and promotes the application and development of multimodal interaction technology in the medical field.