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The landing taxiing instrument light of civil aircraft is an important part of the fault detection system. Because the electrical interface of civil aircraft is vulnerable to electromagnetic interference during the ground taxiing process, the landing taxiing instrument light is always on, which increases the potential safety hazard during flight. For this reason, a correlation detection method for the constant light fault of the taxiing instrument lights of civil aircraft landing is proposed by introducing the IBA-LSSVM forced selection model. Analyze the cause of the instrument light constant lighting fault, use Python programming data to collect the lighting signals and electrical cross-linking data during aircraft takeoff, approach, landing and ground taxiing, and establish a data association detection logic relationship database through control logic. The bat algorithm (IBA) is improved by using adaptive Doppler compensation method to detect the fault characteristics of the landing taxiing light always on. In the forced selection part, the IBA-LSSVM model is constructed. The distributed game theory and linear output regulation theory are combined to eliminate external interference, and the landing taxiing light always on fault detection is completed. Matlab simulation test results show that the fault identification rate of the proposed method is above 95%, the fault identification accuracy can reach 97.3%, and the fault identification time is less than 2ms, which can effectively identify fault data and reduce flight safety hazards.