The invention relates to an intelligent automobile rapid test method based on Bayesian optimization. According to the method, representative test scenes are accurately selected, the test frequency isreduced, and the safety of a large number of samples is evaluated. The method comprises the following steps: firstly, obtaining driving scene key parameters of a vehicle on the basis of on-site traffic scenes, determining a value range and a sampling interval for the key parameters, and combining the key parameters to form a parameter space; then, based on the Bayesian optimization theory, selecting appropriate classifiers and acquisition functions according to different test purposes; and finally, initializing a classifier, calculating a numerical value of an acquisition function, and selecting a next intelligent automobile test scene which is more in line with requirements according to the numerical value of the acquisition function. Compared with the prior art, the method has the advantages of reducing the test times, improving the test efficiency, ensuring the test reliability and the like.