Currently, AI application workloads, represented by machine learning, exhibit a dual-density characteristic, combining both compute-intensive and data-intensive traits. These applications not only require support for the storage, transmission, and fault tolerance of massive data but also need to optimize the performance of complex logical computations. Traditional single big data frameworks or high-performance computing frameworks can no longer meet the challenges posed by these applications. The hybrid big data platform based on Spark and MPI proposed in this paper is a high-performance big data processing platform. This platform, built on a typical large-scale cluster, focuses on addressing the storage and computing characteristics of dual-density applications, such as those in machine learning, and includes three key modules: dual-paradigm hybrid computation, heterogeneous storage, and integrated high-performance communication. To address the dual-density nature of these applications, which involve both data-intensive big data processing and compute-intensive high-performance computing, a computational module combining the Spark and MPI paradigms is designed. By splitting and classifying tasks, compute-intensive tasks are offloaded to the MPI computation module, enhancing the dual-paradigm hybrid computation capability. To address the different data characteristics during the computation process, a heterogeneous storage structure and a data-metadata separation strategy are designed. This optimizes data storage through classification, building a highly available, high-performance storage system. In response to the communication needs of dual-density computing, a high-performance communication technique is proposed, providing strong communication support for the computing and storage modules. Test results show that this platform provides efficient dual-paradigm hybrid computation for dual-density applications, achieving performance improvements of 4.2% to 17.3% compared to a standalone Spark big data platform for various computation tasks.