This study aims to improve the recognition of perimeter intrusion events of the optical fiber sensing system under complex outdoor conditions. An intrusion event recognition method based on improved singular spectrum analysis and genetic algorithm optimized bidirectional long-short-term memory neural network (GA-BiLSTM) is proposed. First, the improved singular spectrum analysis was used to iteratively denoise the optical fiber sensing signal and its components. The signal contribution rate was used to determine the order of signal reconstruction, which controls the denoising process of the signal components, thereby completing the denoising of the optical fiber sensing signal. To recognize intrusion events, the genetic algorithm was used to optimize the parameters of the neural network. Subsequently, a bi-directional long-short-term memory neural network was constructed to extract the spatial characteristics of optical fiber signals. An intrusion event recognition experiment was carried out using the measured optical fiber sensing signals of six events, i.e., climbing, running, knocking, static, windy, and rainy days. The experimental results show that the improved singular spectrum analysis, when applied to the dual Mach-Zehnder fiber perimeter sensing system, exhibits superior denoising performance compared to ordinary singular spectrum analysis. The average signal-to-noise ratio of the consequent signal improved by 12.79 dB. However, the mean root mean square error was slightly reduced. Moreover, the GA-BiLSTM method increased the average recognition rate of intrusion events by 5.7%, with the recognition accuracy rate reaching up to 98.1%.