Mental health issues represent a major public health and social problem that significantly impacts economic and social development. Compared to other diseases, mental disorders can impair various aspects of a patient' s life, including psychological, social, occupational, and educational functions, affecting their quality of life and daily living abilities. Particularly, severe mental disorders tend to have a chronic course, often resulting in diminished social functions and social withdrawal, making it difficult for patients to integrate into society. Repeated, systematic, and comprehensive rehabilitation training for patients with severe mental disorders can effectively control or delay disease recurrence, improve social functions, enhance quality of life, and facilitate patients' reintegration into society. In recent years, the scope of mental disorder rehabilitation has expanded to include enhancing patients' social functions and promoting their integration into society. Vocational rehabilitation and social skills training are widely used in the rehabilitation treatment of patients with severe mental disorders, and some physical intervention methods, such as neurofeedback training, have also proven to be significantly effective in the rehabilitation process. However, traditional rehabilitation techniques often lack specificity and fail to meet individualized needs of patients. Additionally, the rehabilitation process lacks long-term monitoring, making it challenging to continuously assess and adjust patients' rehabilitation outcomes. Furthermore, the assessment of rehabilitation effectiveness mainly relies on patients' subjective feelings and clinical observations, lacking high-quality evidence. Therefore, there is an urgent need to introduce new rehabilitation technologies and scientifically evaluate their effectiveness to address the shortcomings of traditional methods and provide more personalized, precise, and effective rehabilitation support. With the rise of digital health technologies, the field of mental health rehabilitation has encountered new opportunities. Compared to traditional therapies, digital health is revolutionizing the healthcare industry, moving away from traditional approaches to healthcare management to real-time personalized monitoring and therapeutic care.Technologies such as remote monitoring, virtual reality, and computer-assisted cognitive correction therapy are increasingly applied in rehabilitation. However, these methods still need improvements in data management and integration capabilities. A large amount of data accumulates in systems, recording only the training process and real-time effects of patients, without further evaluating their rehabilitation status, leading to resource waste. Therefore, there is an urgent need to develop a digital rehabilitation model that better meets the genuine needs of patients with severe mental disorders. This study aims to integrate multimodal technology, reinforcement learning, and agent-based modeling (ABM) into the research of mental health rehabilitation to more accurately assess and predict the rehabilitation status of mental disorder patients and to more effectively guide and support decision-making in mental rehabilitation treatment.