Aim: Clustering belongs to unsupervised learning, which divides the data objects in the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity. Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm. Method: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing partitional based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: Efficiency and superiority of the new model are verified by comparing with the clustering results of the DBSCAN algorithm.

Peer-to-peer systems nowadays are widely used because of the scalability and high reliability. File replication and consistency maintenance are widely used techniques to achieve high system performance. These techniques are connected to each other. The connection of these techniques is consistency maintenance is needed in file replication to keep the consistency between a file and the replicas. Traditional file replication and consistency maintenance methods need a high cost. The usage of IRM (Integrated file Replication and Consistency Maintenance inP2P systems) which will achieve high efficiency at a significantly lower cost can be used to solve this problem. IRM reduces redundant file replicas, consistency maintenance overhead, and unnecessary file updates.

Aim: To propose a set of dynamic model generation algorithm DPGA, the algorithm can generate parameter models and service models based on user scenarios. Background: Buildings in the traditional sense have become increasingly unable to meet modern humans’ pursuit of high-quality living and working environments, with the pace of urban development, modern buildings have gradually entered people's lives Objective: Research on Electronic Data Energy Consumption Monitoring System Based on the Construction of Internet of Things Method: The author made two definitions of the communication format between the middleware and the wireless sensor network, the author has designed the software and hardware functions of the nodes of the system's wireless sensor network, the author implements part of the node. Finally, the author describes the specific implementation of the application program interface and data interface between the modules of the middleware system, and take the internal environment of a typical office building as an example, the author discussed the deployment plan of system nodes in specific environments and the division of similar areas. Result: It has been verified that the platform is committed to strict monitoring and management of energy-consuming equipment Conclusion: Realize the reasonable distribution of energy consumption, energy saving, and humanized and automated energy consumption monitoring functions in the office area of large office buildings in modern cities.