To address the limitations of unmanned aerial vehicle vision-and-language navigation (UAV-VLN) in multimodal feature fusion, global environment perception and long-distance planning, a history-enhanced dual-scale mixture-of-experts network is proposed. The proposed framework constructs a dual-scale structure composed of a global mixture-of-experts network and a local mixture-of-experts network. Specifically, the global network integrates linguistic descriptions with navigational map features to perform global semantic reasoning, while the local network combines visual observations and linguistic features to enable fine-grained semantic perception, thereby enhancing cross-modal understanding at different scales. To strengthen temporal modeling and long-term dependency capture, a historical memory encoder based on a matrix long short-term memory (mLSTM) network is introduced to dynamically fuse historical observations with the current state. Furthermore, a goal-progress co-prediction module is designed to jointly estimate the agent′s spatial position and task stage through a goal prediction head and a progress prediction head, thereby improving the continuity and interpretability of navigation decisions. Experiments conducted on the city-scale UAV-VLN CityNav dataset demonstrate that the proposed method achieves a success rate (SR） of 16.42% and a success weighted by path length (SPL） of 18.26%, outperforming the baseline model by 10.04% and 12.18%, respectively. These results verify that the proposed approach substantially enhances cross-modal understanding, temporal reasoning and long-distance navigation capability under urban scenarios.