Abstract: The accurate identification capability of the optical fiber sensing pre-warning system, as a core technology for intelligent protection of long-distance oil and gas pipelines, directly impacts the efficiency of third-party construction risk prevention and control. To address challenges such as high misjudgment rates of vibration signals, ambiguous risk classification, and significant regional variations due to interference in complex geographical environments, an intelligent pre-warning optimization solution based on deep learning was proposed. A three-tier system architecture of "terminal-edge-cloud" was established, integrating a large AI model recognition algorithm to accurately distinguish vibration events from effective alarms. Algorithm optimization and threshold configuration significantly enhanced risk identification accuracy and system adaptability under activity interference. The response was speeded up through optimized management and control strategies, along with pre-warning classification and grading mechanisms, establishing a new paradigm for third-party damage risk prevention and control with "optical fiber safety pre-warning + manual patrol"