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Abstract: The current unsupervised component support vector machine model detection technology and high-dimensional random matrix detection technology are used. When the target data of network communication service is detected, the special attribute target acquisition process is lacking, resulting in poor data detection. Aiming at this problem, this paper proposes a research on network communication service target data detection technology based on joint compressed sensing reconstruction. According to the principle of joint compressed sensing reconstruction, the temperature sparse target data of the network communication service node is collected, and the joint compressed sensing reconstruction technology is used to process the communication data of the network node, and the sparse binary matrix is ??constructed to complete the accurate reconstruction of the unknown quantity detection data. The constructor is used to calculate the similarity between network communication service data, to distinguish different sample features, to eliminate unnecessary data features, and to use joint compressed sensing reconstruction technology to detect network communication service target data. The experimental results show that the detection rate of the technology data can reach 87%, which lays a foundation for the data mining of social network special objects under massive data.