The invention discloses a LDW false and omitted alarm test method based on a convolutional neural network (CNN). The method comprises steps of S1, disposing a camera; S2, setting a maximum lateral distance L, and averagely discretizing the same into n categories; S3, acquiring a real-time image A, inputting the same to a deep CNN model, calculating the actual distance di of a lane line; S4, determining whether a LDW system has false or omitted alarms; and S5, obtaining the misoperation rate of the LDW system. A test system comprises an image acquisition device, an onboard data acquisition mechanism, an analyzer, and an operation processor. The image acquisition device is connected to the analyzer, and the operation processor is connected to the analyzer and the onboard data acquisition mechanism. The method is easy to operate, high in recognition speed and high in recognition precision, applicable to the lanes in various road conditions. The test system can be just provided with the image acquisition device, the onboard data acquisition mechanism, the analyzer and the operation processor in the simplest manner, can fully automatically identify deviations without an extra lane linemark ruler.