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In order to solve the problem of path deviation and non-optimal path in the traditional biological-inspired neural network (BINN) in point-to-point global path planning, BINN based on path modification and ideal barrier-free path guidance was proposed. In the initial stage of path planning, determine whether to trigger the path generation strategy by judging the external stimulus input of the starting unit and the activation value of the starting unit, so as to achieve the initial path modification; combine the guidance of the ideal path without obstacles in the algorithm for generating the next position unit The introduction of the ideal path approach rate between the actual path unit and the barrier-free ideal path unit increases the path neuron activity value, thereby achieving the purpose of optimizing the path. In a static and complex environment, three experiments were carried out for comparison experiments. The experimental results show that the improved path planning algorithm not only solves the problem of path deviation in the initial stage of path planning, but also makes the path length and number of path turns lower than traditional biological inspired neural network algorithms and target-guided biological inspired algorithms. higher efficiency.