The highway polysyllabic path problem refers to how to determine a driving path of a vehicle in a highway network with multiple optional paths. At present, the identification point-based polysemy path identification method commonly used in some cases (such as equipment failure, ambient brightness or insufficient transparency) has a low recognition rate, which makes it difficult to identify the vehicle polysemy path in some time periods. Aiming at the above situation, this study proposes a multi-sense path probability identification method based on historical data. The road segment-based clustering method is used to calculate the probability values of each road segment, and then the greedy algorithm is used to find the vehicle's driving path, which is used to identify equipment faults. It assists in identifying polysemy paths. The method can effectively identify the ambiguous path when identifying the equipment failure, and improves the accuracy of the method.