The distribution of nodes in ultra large capacity fiber optic communication networks is relatively chaotic, and the correlation between nodes is complex, resulting in low accuracy in security domain division, seriously threatening the security of fiber optic communication networks. To this end, a security domain partitioning method for ultra large capacity fiber optic communication networks is proposed. According to the fiber optic network environment, the K-means algorithm is used to set the initial security center value, calculate the distance between the representative sample value in each region and this value, and solve for the average standard values of the maximum distance and the shortest distance. On this basis, the particle swarm separation method is used to identify intrusion signals, and the changes in membership values between each node and the set local area are calculated separately under normal and presence of intrusion particle interference, in order to determine the phase difference of the signal. Based on the correlation value between nodes, the node area that matches the dissimilarity inverse vector is divided to complete the security domain division. The experimental results show that the proposed method has high accuracy in partitioning, and after the implementation of the algorithm, the number of nodes participating in attack events significantly decreases, and network security is strengthened.