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Aiming at the problem that the traditional fuzzy feature detection method is not suitable for the current application, a new method of network security protection situation optimization based on data clustering is proposed. Firstly, a network security state distribution model is constructed, and the big data mining method is adopted to data mining network security information. Secondly, the new intrusion identification detection method is used to carry out adaptive feature extraction for the designed network security estimation state, and extract the characteristic data set and processing unit of the network security state. Then, fuzzy-C average data clustering method is used to extract comprehensive information. The intrusion characteristic information flow is classified, and the network security situation is predicted according to the attribute classification results to realize the security situation evaluation. Finally, experiments are carried out in different scenarios. The results show that the proposed algorithm is suitable for network security scenarios, and its accuracy and robustness are verified.