Aiming at problems of slow update of 3D aeronautical chart， poor dynamic obstacle avoidance ability and lack of global perception in Unmanned Aerial Vehicle （UAV） navigation， a low-altitude dynamic navigation method based on visual Artificial Intelligence （AI） and 3D grid computing was proposed. In the method， the 3D spatial grid representation system was constructed， grid index was used to represent spatial information associations efficiently， combined with the airborne visual AI model， the environmental semantic information was perceived in real time， and the perceptual results were mapped into the 3D spatial grid in real time through the semantic feature embedding algorithm， so as to support the autonomous obstacle avoidance flight of UAVs. In local grid corresponding to the same sub-level airspace， a self-organizing communication network was constructed to share position， speed， height and other states of UAV in real time， thereby forming a low-altitude dynamic information pool， so that UAVs were able to grasp the neighboring UAV states in high-density areas such as take-off and landing areas accurately. At the same time， the 3D grid was subdivided recursively， and hierarchical search path was used to achieve overload grid intelligent flow distribution through the local grid interactive negotiation protocol. Taking the low-altitude scene of 50 square kilometers of city as the experimental object， three kinds of scenes， simple， medium and complex， were set up to compare the proposed method with Global Navigation Satellite System （GNSS） pure positioning navigation and monocular visual feature point navigation. The results show that in the three scenarios， the average obstacle avoidance efficiency of the proposed method is 96.7%±1.2%， 95.3%±1.1% and 93.8%±1.5%， respectively， which is 10.5， 10.8 and 11.7 percentage points higher than that of GNSS pure positioning navigation， and 26.9， 27.8 and 28.6 percentage points higher than that of monocular visual feature point navigation. It can be seen that the proposed method can solve the core pain points of UAV navigation effectively， and improve navigation efficiency and safety in complex low-altitude environment significantly.