Earthquake is one of the most destructive natural disasters, it can not only cause heavy casualties and economic losses, but also may even lead to serious secondary disasters. As the main bearing body in earthquake, buildings often suffer serious damage, so they can be used as an important reference for post-earthquake disaster loss assessment. Timely and accurate acquisition of regional earthquake damage information after an earthquake is of great significance for scientific and effective emergency rescue and disaster loss assessment. At present, the main methods for earthquake damage identification can be roughly divided into two categories: 1) Manual visual interpretation investigation method. It takes a lot of time for manual field investigation or manual identification of earthquake damage images to process a large amount of seismic damage information in a short period of time, and it is likely to lead to inconsistent discrimination standards for seismic damage of buildings. 2)Image recognition method based on satellite image or UAV image. The recognition method based on satellite remote sensing image after the quake identifies earthquake damage by the texture, brightness and other characteristics of the image of the seriously collapsed buildings, thus, it can quickly get the seismic damage situation in a large area, but as results of offset, low resolution and poor timeliness of the satellite image, it is hard to identify the slightly overlaying and cracking of tiles on the roof of buildings. The combination of high-resolution image obtained by UAV and machine learning algorithm can not only reduce the labor input, but also bring a high accuracy rate. Therefore, based on ant colony algorithm(ACO)and high-resolution remote sensing image of UAV, this paper proposes a new method to efficiently identify the earthquake damage of buildings in the study area, which was applied and verified in the recent Yangbi M6.4 earthquake in Yunnan Province. By improving the update strategy of pheromone concentration in ant colony algorithm and introducing the optimization operator, the better identification rules are established, and the speed and accuracy of earthquake damage identification are enhanced. The UAV high-resolution image of Yangbi county seat was obtained the first time after the Yangbi, Yunnan Province, M6.4 earthquake took place, and taking the image as experimental data, the extraction effect of regional earthquake damage is verified, and compared with ant colony algorithm and maximum likelihood method. The results show that the proposed earthquake damage identification method based on improved ant colony algorithm and UAV high-resolution image can effectively improve the identification accuracy and efficiency of damaged buildings in the region, which is of great significance for post-earthquake emergency rescue and providing accurate disaster information.