On June 20, 1995, the State Council promulgated the "Outline of the National Fitness Plan". Its promulgation is a major decision for the country to develop social undertakings. It is a national leadership and national participation, with purpose, tasks and measures. The fitness plan is a feat of "contributing to the present and benefiting the future". It is a social system project supporting the goal of realizing socialist modernization. Based on this goal and based on the needs of the majority of citizens in the current economic and social economy, my team and I proposed to develop the Hmove fitness and entertainment platform. This study aims to explore the effectiveness and practicality of personalized fitness plans based on the Hmove platform. The Hmove platform, as an innovative fitness platform, integrates advanced data analysis and user feedback mechanisms to provide powerful technical support for the formulation of personalized fitness plans. The research collects users' basic information, fitness goals and preferences, and uses data mining and machine learning technology to customize fitness plans for users. During the experiment, we used questionnaires to interview users who frequently use fitness APPs, continuously tracked their fitness progress and collected feedback. The results showed that participants' physical fitness and health status were significantly improved under the guidance of personalized fitness plans. At the same time, most participants were highly satisfied with the fitness program and felt that the program met their needs and expectations. However, the study also found that some users encountered some challenges in the execution of the plan, such as difficulties in time management and insufficient flexibility in plan adjustment. These questions remind us that we need to take more into consideration the actual situation and dynamic needs of users in the formulation of future personalized fitness plans. Looking to the future, we recommend in-depth research on how to combine real-time feedback and data from users to further optimize the personalized fitness plan algorithm and improve the adaptability and flexibility of the plan, thereby improving user experience and fitness effects.