The current progress of intelligent decision-making in domestic enterprises is slow, making it difficult to quickly enhance the core competitiveness of enterprises. This will directly affect the pace at which Chinese enterprises integrate with international modernization development. The main manifestations are as follows: 1) The computational architecture of intelligent decision-making is outdated. Traditional information technology uses C/S or B/S architectures, which have high construction costs, severe information silo effects, and do not support high-performance computing for decision-making, leading to a backward foundation for intelligent decision-making systems. 2) Data management is lagging behind. There are serious internal data barriers within traditional enterprises, lack of data standards, and data heterogeneity that directly hinders data sharing and linkage analysis, thus making it difficult to provide effective data for intelligent decision-making. 3) Intelligent decision-making technology innovation is challenging. Traditional decision-making technologies cannot support high-performance, parallelized computing, and the capabilities for data acquisition and processing do not improve qualitatively, unable to meet the decision-making requirements of the current big data environment. This leads to low decision-making efficiency. In response to the aforementioned issues, this project, funded by national programs such as the 863 Plan and the National Natural Science Foundation, aims to achieve computational architecture serviceization for intelligent decision-making, semantic data management, and innovative decision-making technology. After more than a decade of research and development, it has constructed key technologies and systems for enterprise intelligent decision support driven by data semantics. Through cloud computing technology-supported enterprise intelligent decision-making infrastructure, semantic-driven enterprise decision big data management, and massive case reasoning-based intelligent decision-making technology, it realizes high performance, high sharing, and high efficiency in enterprise intelligent decision-making, ultimately achieving the scientific modernization of enterprise decision analysis. The main innovations are as follows: 1) It has realized ontology-based semantic understanding and refinement of decision problems, and achieved intelligent collection of decision case big data based on semantics, building a massive decision case library. Based on this, the semantic-based decision case intelligent reasoning technology is proposed to achieve enterprise intelligent decision-making. 2) Establish an enterprise decision data standard system and data model, propose ontology-based semantic enterprise decision data collection, representation, storage, and mining technologies, enabling scientific management and efficient sharing of enterprise decision data. 3) Complete the intelligent construction of decision models based on semantic differences, implement model selection through the ability and confidence evaluation of decision models, and deploy a dynamic combination strategy for decision services at the platform layer. 4) Propose an enterprise intelligent decision 'cloud computing four-layer' system, which innovatively introduces 'Data Management as a Service (DMaaS)' and 'Decision as a Service (DSaaS)' on the basis of traditional 'IaaS' and 'PaaS'. Achieve elastic management of virtualization resources at the bottom layer of cloud platforms and comprehensively optimize the intelligent decision-making infrastructure. The above core innovations are hierarchically ordered and interdependent, forming a systematic enterprise intelligent decision support solution with distinct technical characteristics. The project has obtained 2 authorized invention patents, 6 accepted invention patent applications, formulated 2 enterprise standards, and secured 5 software copyrights; it has published over 50 papers, including more than 30 SCI/EI indexed articles and more than 160 citations on Google Scholar. Currently, it is applied in 10 provinces, municipalities, and autonomous regions across the country, such as Shanghai, Beijing, Guangdong, Shaanxi, Shanxi, Xinjiang, etc., serving more than 600 engineering projects involving national large and medium-sized enterprises, government agencies, institutions, and small and medium-sized enterprises, including China Shenhua Group and its subsidiaries, the National Bureau of Statistics, the Shanghai Bureau of Statistics, etc. Promoted the rapid transformation of informatization and scientific management in multiple units, optimized business processes, improved decision-making support efficiency, and generated significant social and economic benefits. From January 2011 to December 2013, the project cumulatively added new output value of 591.0273 million yuan, new profit of 59.1384 million yuan, and new tax revenue of 26.4486 million yuan.