<p>The distributed operation and maintenance control system generates a huge amount of data, and traditional data processing techniques are difficult to efficiently process and store this massive amount of data, resulting in high latency and slow response. The information processing technology integrates various new technologies such as artificial intelligence, big data, and cloud computing, resulting in smoother network operation, larger storage capacity, and better compatibility of network system platforms. Therefore, this article aims to apply information processing technology for real-time data analysis in distributed operation and maintenance control systems, in order to improve data processing speed, system stability, and resource utilization. Firstly, the data collection architecture of the distributed operation and maintenance control system is designed, using a hierarchical architecture to ensure real-time and reliable data. In terms of real-time data processing, the Apache Kafka is used as the message queue system, combined with Apache Flink for data stream processing. In terms of data storage and management, the Cassandra is used as a distributed database, combined with Remote Dictionary Server (Redis) for fast data caching, ensuring high availability and fast access. Then a real-time data analysis model that covers prediction, classification, anomaly detection, fault diagnosis, and recovery functions is built. Finally, three indicators are selected for validation. The results show that the application of information processing technology has fast data processing speed and good system stability, with a resource utilization rate of over 93%. This provides strong data support for information processing technology to improve the efficiency and user experience of enterprise operation and maintenance systems.</p>