Breaking isolated information island, integrating heterogeneous data, gathering and sharing exchanges, conducting in-depth analysis and mining, and providing industry-side decision-making and situation analysis have far-reaching theoretical and applied value. Based on the actual demand of the situational awareness service of the Chinese Academy of Sciences, this study designs and implements a Hive-based Hadoop/Spark dual computing engine big data warehouse supporting OLAP analysis in multiple ways, and carries out an optimization design of usability, load balancing, and resource management, which provides platform support for the subsequent data aggregation and mining, knowledge map construction and discipline situation analysis. Experimental results show that the system is flexible, efficient, available, and scalable, the resource scheduling is scientific, and the load balancing effect is obvious.