Fiber optic pulse coherent optical communication systems are subject to multipath interference in information transmission, resulting in poor detection and recognition capabilities for network abnormal intrusions. In order to improve the accurate identification of abnormal intrusions in optical communication systems, a clustering analysis based online identification method for abnormal intrusions in optical communication systems is proposed. Firstly, the cellular sensing distributed detection technology is used to realize the packet collection of the transmission data of the optical communication system. Secondly, the collected data is preprocessed to remove multipath noise interference. Then, the Ensemble learning algorithm is used to extract the characteristics of the abnormal intrusion data of the optical communication system, and calculate the relevant parameters of the abnormal intrusion data of the optical pulse coherent optical communication system. Finally, the extracted characteristics are clustered, Through Spectral clustering analysis, the online identification of abnormal intrusion in fiber optic pulse coherent optical communication system is realized. The simulation results show that the proposed method has high efficiency in identifying abnormal intrusions, takes 5 seconds to identify, maintains recognition accuracy at around 98%, consumes 38 J of energy, is less affected by disturbances, and has strong resistance to harmonic interference.