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Aiming at the given path planning method for mobile robot has the slow convergence rate and hard to plan on-line, a dynamic path planning method based on state aggregating SOM net and Q-Learning is researched. Firstly, the planning model of whole system is designed and it is divided into two parts such as frontier part (state aggregating) and back part (path planning), then the three-layer SOM net is developed to realize the aggregation of states based on the traditional SOM, the training algorithm for three-layer SOM net is given. Finally, a algorithm for obtaining the optimal strategy based on eligibility trace and adaptive changing explore factor is proposed, and the number of output nodes of SOM can be adaptive increase or decrease according to the convergence extent of the Q(λ), therefore, the whole convergence can be guaranteed by the improved algorithm. The simulation experiment shows the method designed can realize the path planning, and compared with the other methods, it has the rapid convergence rate and the ability to get the optimal solution, and it is proved to be has big priority over the other methods.