Protecting national and provincial key protected species and their habitats is the crucial standpoint of biodiversity conservation in China. Efficient and accurate identification of priority conservation areas (PCAs) for these most deserving species facilitates the achievement of national and regional biodiversity conservation goals. This study proposes an algorithm that combines the sorting approach based on species compositional complementarity with probabilistic sampling to identify PCAs. To enhance protection for species with higher conservation value and narrow range, our algorithm incorporates weight scores based on the regional conservation category of each target species, as well as habitat area protection targets. We applied the algorithm to delineate PCAs in Huairou District, Beijing, aiming to attain protection rates of 80%, 95%, and 100% for 222 key protected species, the results showed that the PCAs exhibited a fragmented distribution pattern in the district, occupying 5.92%, 9.10% and 10.83% of the total study area to achieve the respective protection rate targets. By spatially overlaying the PCA maps with the ecological protection redline map. Conservation gaps were primarily located in the southern region, which represented a critical hotspot for key protected avian species. Compared to hotspot-based PCAs identification approach, our approach demonstrates greater cost-effectiveness in targeting key protected species. Given substantial interspecific variation in spatial distributions, the PCAs identified by our approach exhibit a discrete, patch-like distribution pattern. Site-based conservation should be integrated with regional ecological protection, ensuring effective conservation and management of key species with great importance, while concurrently mitigating impacts from human disturbances and habitat loss.