A visual efficacy determining method for non-coloured objects in different light environments, characterized in that, the method comprises the following steps: (a) setting light environmental parameters of a tunnel middle section, wherein the light environmental parameters include color temperature T, color rendering index R and average brightness L, selecting and setting a set of color temperature T, color rendering index R and average brightness L as the light environmental parameters; (b) placing a non-coloured visual object in the tunnel middle section, wherein the visual object is a cube of which the side length is C, and selecting a plurality of drivers to be tested at a certain position in the tunnel middle section, the distance between the position of the visual object and the position of the tested driver is D, and the tested driver's visual height is H; and after each tested driver puts on a dynamic eye movement device which has been calibrated, starting to record an eye movement video, blocking the tested driver's front scene with an obstruction, randomly placing the visual object, removing the obstruction, instructing the tested driver to start searching and visually recognize the visual object, stopping the video recording after the visual recognition is completed, and recording the visual recognition results; (c) resetting a different set of light environmental parameters of color temperature, color rendering index and average brightness for the tunnel middle section, and repeating step (b) to obtain a plurality of drivers' related test information data and results under multiple sets of different light environmental parameters; (d) analysing the tested driver's dynamic eye movement video acquired by the dynamic eye movement device to determine the number of frames in visual fixation points with which the driver captures the visual object, and determining visual recognition time of the driver visually recognizing the object under different light environmental characteristics with the number of frames of the visual object, and establishing a relationship analysis between the visual recognition time and the color temperature, the color rendering index and the brightness parameter of the light environment by correlating the visual recognition information data; (e) performing a visual efficacy analysis by analysing the relationship between the visual recognition time and the color temperature, the color rendering index and the brightness parameter of the light environment, to obtain light environmental parameter standards for improving the visual efficacy of non-coloured objects under certain combinations of the color temperature, color rendering index and light source brightness parameters. (a) setting light environmental parameters of a tunnel middle section, wherein the light environmental parameters include color temperature T, color rendering index R and average brightness L, selecting and setting a set of color temperature T, color rendering index R and average brightness L as the light environmental parameters; (b) placing a non-coloured visual object in the tunnel middle section, wherein the visual object is a cube of which the side length is C, and selecting a plurality of drivers to be tested at a certain position in the tunnel middle section, the distance between the position of the visual object and the position of the tested driver is D, and the tested driver's visual height is H; and after each tested driver puts on a dynamic eye movement device which has been calibrated, starting to record an eye movement video, blocking the tested driver's front scene with an obstruction, randomly placing the visual object, removing the obstruction, instructing the tested driver to start searching and visually recognize the visual object, stopping the video recording after the visual recognition is completed, and recording the visual recognition results; (c) resetting a different set of light environmental parameters of color temperature, color rendering index and average brightness for the tunnel middle section, and repeating step (b) to obtain a plurality of drivers' related test information data and results under multiple sets of different light environmental parameters; (d) analysing the tested driver's dynamic eye movement video acquired by the dynamic eye movement device to determine the number of frames in visual fixation points with which the driver captures the visual object, and determining visual recognition time of the driver visually recognizing the object under different light environmental characteristics with the number of frames of the visual object, and establishing a relationship analysis between the visual recognition time and the color temperature, the color rendering index and the brightness parameter of the light environment by correlating the visual recognition information data; (e) performing a visual efficacy analysis by analysing the relationship between the visual recognition time and the color temperature, the color rendering index and the brightness parameter of the light environment, to obtain light environmental parameter standards for improving the visual efficacy of non-coloured objects under certain combinations of the color temperature, color rendering index and light source brightness parameters.