Mobile robot navigation based on a simple learning mechanism is generally applied to static scenarios and has poor adaptability. Therefore, we propose a method of adaptive navigation under a dynamic scenario. In the method, we propose a local obstacle avoidance link to the maximum distance priority mechanism, on the basis of a simple learning mechanism, using an incremental hierarchical discriminant regression（IHDR) algorithm, and acquire environmental distance information with a laser range finder（LRF). This overcomes the over-dependence on the environmental model in traditional navigation methods, and simultaneously resolves the problem of poor adaptive capacity in dynamic scene navigation with a simple learning-based mechanism, using the proposed local obstacle avoidance algorithm. We apply the proposed navigation method to an MT-R robot, and compare this with the experimental results from a learning-based navigation method. In addition, an algorithm analysis experiment is performed on LRF data using the proposed local obstacle avoidance algorithm. The results illustrate the feasibility of the proposed method, and reveal its effectiveperformance in dynamic scenarios.