To address the limitations of traditional Coriolis-force experiments, such as coarse measurements and limited functionality, this study develops an intelligent platform for the demonstration and investigation of Coriolis dynamics. The platform adopts an end-cloud collaborative distributed architecture: the front end consists of an STM32 control unit and a K230 vision-acquisition module for multi-source data collection, while the PC-based back end integrates Bluetooth and Wi-Fi data transmission. By incorporating a YOLOv8-based object-detection model, the system enables real-time trajectory tracking and synchronous comparison between theoretical predictions and experimental measurements. Performance evaluation shows that the platform achieves a spatial resolution of 0.667mm/pixel, an end-to-end latency of less than 1.320s, and an R² value above 0.99 between YOLO-extracted experimental trajectories and theoretical trajectories over 50 repeated trials under identical conditions, demonstrating high precision, real-time performance, and strong reliability. Through two representative experiments—ball-motion deflection and fluid erosion—the platform supports both intuitive visualization of classical physical laws and in-depth exploration of complex nonlinear phenomena. By integrating the Mamba model with physics-informed neural networks (PINNs), together with semi-supervised learning and self-attention mechanisms, the platform can predict the outcomes of fluid-erosion experiments and infer the dominant physical mechanisms governing the erosion process directly from experimental data. This work provides a complete and practical solution for the modernization and intelligent upgrading of traditional physics experimental platforms.