The invention provides a lane-level road mapping method based on crowdsourcing space-time big data, and the method comprises the steps: building a similarity estimation model of a track vector, carrying out the track optimization based on a growth clustering method integrated with experiential knowledge, building a Gaussian constraint mixed model, and employing an EM algorithm to solve model parameters; detecting lane information, and obtaining an initial detection result of the number of road lanes; correcting the initial detection result based on a road construction rule; and correcting the central lines of the lanes according to the corrected number of lanes and the conditions of the surroundings. The method reduces the cost in obtaining the urban precise road information, is simple, and is easy to implement.