The existing power index benchmarking evaluation platform has large deviations in evaluation results and poor real-time performance due to the difficulty in integrating multi-source heterogeneous data, lack of index standards and lagging analysis mechanisms, which affects the scientific evaluation of power system operation performance. To solve this problem, this paper proposes an optimization solution based on cloud computing and big data technology. The innovation lies in the deep integration of standardized index system construction and intelligent benchmarking algorithm into the platform architecture. In the method design, Kafka and Flume are used to access data sources such as SCADA (Supervisory Control and Data Acquisition) and metering systems in real-time. Hadoop and Spark are used to complete data preprocessing and unified modeling. A unified index data warehouse is built based on Hbase (Hadoop Database) and Hive. A benchmarking evaluation model with cluster analysis and weighted scoring as the core is designed, and visualization and intelligent recommendation are realized under the Spring Boot framework. The experiment is conducted on a measured dataset of a regional power grid. After optimization, the response time of the platform is reduced to 51.5 seconds at a data scale of 100GB. The accuracy of the indicator benchmarking for the industrial park scenario reaches 86.4%, and the accuracy of low voltage anomaly detection is increased to 94.8%. The research results show that this method has significant practical value in improving data processing efficiency, enhancing evaluation accuracy, and supporting management decisions, and has a positive role in promoting the construction of intelligent management platforms in the power industry.