When the traditional ant colony algorithm is selected to solve the robot path planning problem, there are a series of problems, such as non-convergence of results and low planning efficiency. How to improve the algorithm performance and rational path planning is crucial. Combined with the idea of elite ants, an improved ant colony algorithm based on behavioral strategy is proposed. The algorithm selects the favorable grid for the next walk in real time according to the relative position of the current grid where the ant is located and the target grid, which ensures the efficiency of the ant colony algorithm in path finding. The method of establishing and designing the grid model and the principle of the traditional ant colony algorithm are introduced and the defects of the traditional ant colony algorithm are analyzed. Based on the relative positions of ants and target points, 10 behavioral strategies are proposed and incorporated into the ant colony algorithm. Several comparison tests are conducted in raster maps with different obstacle areas and different target points. By comparing the planning results with traditional ant colony algorithm and elite ant colony algorithm, it is proved that the improved ant colony algorithm based on behavioral strategies has better performance and higher planning efficiency.