The traditional branching strategies of Maximum Common induced Subgraph (MCS) problem rely on the static properties of graphs and lack learning information about historical searches. In order to solve these problems, a branching strategy based on vertex conflict learning was proposed. Firstly, the reduction value of the upper bound was used as the reward to the branch node for completing a matching action. Secondly, when the optimal solution was updated, the optimal solution obtained actually was the result of continuous inference of the branch nodes. Therefore, the appropriate rewards were given to the branch nodes on the complete search path to strengthen the positive effect of these vertices on search. Finally, the value function of matching action was designed, and the vertices with the maximum cumulative rewards would be selected as new branch nodes. On the basis of Maximum common induced subgraph Split (McSplit) algorithm, an improved McSplit Reinforcement Learning and Routing (McSplitRLR) algorithm combined with the new branching strategy was completed. Experimental results show that, with the same computer and solution time limit, excluding the simple instances solved by all comparison algorithms within 10 seconds, compared to the state-of-the-art algorithms of McSplit and McSplit Solution-Biased Search (McSplitSBS), McSplitRLR solves 109 and 33 more hard instances respectively, and the solution rate increases by 5.6% and 1.6% respectively.