With the expansion of chip scale and the strengthening of function， the difficulty of verifying chips is increasing geometrically. At present， for the functional coverage of multiple combination incentive cases， the industry’s common practice is to calculate it in the form of fragments or slices according to different use scenarios. This method is easy to operate， but it is difficult to perform a complete coverage analysis of the combination of various configurations under random testing. To solve this problem， a verification method based on a machine learning algorithm for fast convergence and strong universality of coverage is proposed. In this method， each configuration incentive is decomposed according to the weight， and the key cross bins in the function coverage are observed. The data set is collected and trained by the feature that the function point analysis does not consume the simulation time. Through the actual test adjustment， an improved network structure is realized， which can predict the coverage rate of various incentive combinations， and also can pick an incentive input that specifies a coverage threshold. Simulation results show that compared with the random case， the proposed method can significantly reduce the simulation time and effectively reduce the simulation resource occupation. Compared with other network structures， the proposed network achieves faster convergence and higher prediction accuracy.