The development of digital technology is facilitating the smart emergence of stroke emergency care. Deep learning MCDE techniques and algorithms based on QoS have been used in this paper to create an integrated digital platform to improve the effectiveness and efficiency of acute ischemic stroke emergency response. The MCDE method learns various heterogeneous medical knowledge to create a stroke medical knowledge graph, which serves as a repository of resources to rapidly query. Together with a QoS-based service selection algorithm, it computes ideal emergency medical intervention combinations. With its application to acute ischemic stroke treatment, the combined platform recorded DTP, DTT, and DNT performance rates of 6.29 +1.03, 20.48+3.74, and 17.35+5.12 respectively. Efficacy of thrombolytic treatment was 91.86%. Integrated digital platform-assisted emergency care allows quicker and more accurate emergency response compared to the conventional emergency interventions.
