Pulmonary embolism (PE) is a highly morbid and fatal cardiovascular disease. Right ventricular dysfunction (RVD) secondary to PE indicates a poor prognosis and serves as a critical basis for risk stratification. Recent studies have shown that over one-third of patients continue to experience RVD one year after PE, with the mechanisms and regression remaining unclear. Although electrocardiography (ECG) is the most commonly used test for cardiac disease, its diagnostic specificity for PE is limited. In recent years, artificial intelligence (AI) has successfully extracted hundreds of features from data that are difficult for the human eye to recognize. The correlation between daily vital signs monitored by wearable devices and functional signs of chronic cardiovascular disease suggests the potential of AI in detecting disease progression. There is a lack of specific markers for right ventricular function post-PE, and the significance and changes of these markers in disease progression have not yet been explored. This study aims to develop a predictive model for the progression of RVD after PE using AI, combining electromyography, wearable devices, and vitality markers. In this prospective cohort study, 500 patients with acute PE at intermediate or higher risk were enrolled. Approximately 200 patients with RVD at discharge were followed for one year, with daily electromyographic data collected using portable electromyographs. Biospecimens were collected at the following time points: admission, discharge, and follow-up at 3, 6, and 12 months and a variety of inflammatory markers were measured using a multifactorial assay on liquid suspension cores. These data were integrated into a continuous disease diagnostic model based on a deep learning restrictive updating strategy. Ultimately, a continuous disease diagnosis and prognosis algorithm was developed, yielding a model for predicting the progression of RVD after PE using multifactorial assays on liquid suspension cores to measure various inflammatory markers.