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In today’s more distributed and disorderly network environment, how to organize this information simply and effectively, so that users can quickly obtain potentially valuable data is a common problem in all fields. The commonly used classification systems are based on genetic algorithms and orthogonal decomposition. These two types of systems have high memory usage and low classification accuracy. Aiming at the above problems, a distributed multidimensional big data classification system based on differential equations is designed. The system design is mainly divided into three parts: the first design system overall framework; the second design system hardware, including multidimensional data integration module, central processing module, storage module, result output and display module; third, designing multidimensional big data according to differential equation Classification software main program. The results show that compared with the big data classification system based on genetic algorithm and the big data classification system based on orthogonal decomposition, the classification accuracy of distributed multidimensional big data classification system based on differential equation is improved by 8.75% and 6.75%, and the system memory occupancy rate is improved. Reduce by 35% and 12%.

Aiming at the problems that the traditional method has long response time to communication network congestion monitoring and the detection effect is not ideal, a real-time monitoring method based on cloud computing for communication network blocking is proposed. Firstly, the communication network monitoring point is established, and the communication data collection process is completed by the radio-frequency receiver. On this basis, the real-time traffic calculation of the collected data is performed to determine the existence of abnormal blocking status in the communication network link, and the precise positioning of the blocking point is obtained. The information thus generates an alarm message to obtain a monitoring result. The real-time and accuracy of the monitoring method are analyzed experimentally. It is found that the monitoring method can control the delay time within 0.2 s and the monitoring error rate is low. It can be seen that the monitoring algorithm has high performance.