In order to improve the efficiency in navigation cognitive mapping and reduce the error of direction information, a method of navigation cognitive mapping based on improved Q-learning algorithm is proposed in this paper. Firstly, Radial Basis Function (RBF) neural network is utilized for trainning the mapping relationship between grid cells and place cells, and the place cells are used to convey space information. Secondly, the improved Q-learning algorithm is used to learn the targetoriented Q-value of the place cell to obtain direction information towards target. Finally, the center of gravity estimation principle is used to generate the targetoriented direction information, constructing the navigation cognition map. The simulation results show that learning rounds of the navigation cognitive mapping generated by this algorithm is reduced from 2 000 to 1 000 compared with the traditional Q-learning algorithm, improving the construction efficiency of navigation cognitive map. Meanwhile, a maximum reduction of 15% in relative error is achieved, improving the precision of navigation cognitive mapping.