This paper studied anti-attack performance testing methods in large network. Anti-attack performance testing process in large networks is different from traditional testing process, which mainly features harbinger attacks, lacking precise characteristic information of determining the attack act. Point to point structure limit characteristics connections, traditional detection method focuses on fixed feature information directly linked to take anti-attack performance testing, once lost contact feature will cause the detection inaccuracy. In order to avoid these shortcomings, this paper proposed an anti-attack performance testing method in large networks based on fuzzy C-means clustering algorithm. Collected relevant data to extract and analysis sample characteristics, the use of fuzzy C-means clustering method for classification of data for further calculations, to gain abnormal behavior pattern data to complete anti-attack performance testing in large network. Experimental results showed that using the proposed algorithm for anti-attack performance testing in large network could greatly improve the accuracy of detection, so as to effectively maintain a large network security, and to provide users with good network environment.